IDENTIFYING COUNTRIES MOVING TOWARDS UNDER/OVER UTILIZATION OF THEIR AGRICULTURE GROSS DOMESTIC PRODUCT (GDP) CONTRIBUTION

I.  Introduction

A.  Executive Summary

Agriculture is the practice of cultivating plants, raising animals, and other related activities for the purpose of producing food, fiber, medicinal plants, and other products used by humans. It involves the management of land, water, and other natural resources to grow crops and raise livestock. The role of agriculture in the world is multifaceted and essential for human survival and well-being. Agriculture plays a vital role in ensuring food security, supporting economic development, reducing poverty, promoting sustainable practices, facilitating trade, and preserving natural resources. It is a fundamental sector that influences various aspects of human life and the overall well-being of societies globally through:

Food Production: Agriculture is the primary source of food for the world’s population. It involves growing a variety of crops, such as grains, fruits, vegetables, and oilseeds, and raising livestock for meat, milk, and eggs. Agricultural practices provide the necessary food resources to feed people globally. According to the Food and Agriculture Organization (FAO) of the United Nations, more than 80% of the world’s food supply comes from agriculture (FAO, 2021). Agriculture encompasses a wide range of food production systems, including crop cultivation, livestock farming, aquaculture, and horticulture. This diversity ensures a variety of food products, meeting different dietary preferences and nutritional needs.

Economic Development: Agriculture is a significant contributor to the global economy. It provides employment opportunities for a large portion of the world’s population, especially in developing countries. According to the International Labour Organization (ILO), agriculture employs over 1 billion people worldwide (ILO, 2020) and is estimated to account for approximately 4% of global GDP (The World Bank, 2023). Agricultural activities contribute to national income, export earnings, and overall economic growth. It forms the foundation of the food supply chain, connecting farmers, processors, distributors, retailers, and consumers through activities such as harvesting, processing, packaging, transportation, and storage, ensuring that food reaches consumers in a safe and timely manner.

Food Security: Agriculture plays a vital role in ensuring food security, which is the availability and access to sufficient, safe, and nutritious food for all individuals. By increasing agricultural productivity, improving distribution networks, and implementing sustainable farming practices, agriculture helps prevent hunger and malnutrition. The global population is projected to reach 9.7 billion by 2050 (The World Bank, 2023); FAO estimates that agricultural production must increase by 70% to meet the food needs of the projected population (FAO, 2011).

Environmental Stewardship: Sustainable agriculture practices promote the responsible management of natural resources, including soil, water, and biodiversity. By adopting techniques such as crop rotation, conservation tillage, and agroforestry, agriculture can minimize negative environmental impacts, preserve ecosystems, and promote long-term sustainability. Sustainable agricultural practices, such as agroecology and climate-smart farming, can reduce greenhouse gas emissions, enhance carbon sequestration, and improve resilience to climate-related challenges. These practices promote environmentally friendly and socially responsible food production while aiming to minimize the negative impacts of agriculture on ecosystems, water resources, and biodiversity (Pretty, J; Benton, T. G; Bharucha, Z. P; et al., 2018).

Rural Development: Agriculture is closely tied to rural communities. It provides livelihoods and economic opportunities for farmers, their families, and rural populations. Investments in agriculture can lead to improved infrastructure, education, healthcare, and overall quality of life in rural areas. Small-scale farmers play a significant role in global food production. According to the International Fund for Agricultural Development (IFAD), about 500 million small farms worldwide contribute to food production, employment, and rural livelihoods (IFAD, 2013).

Bioenergy and Renewable Resources: Agriculture plays a role in the production of biofuels and renewable materials. Crops like sugarcane, corn, and oil palms can be used to produce biofuels, offering alternatives to fossil fuels. Additionally, agricultural by-products can be utilized for the production of renewable materials, such as bioplastics and bio-based chemicals. The use of renewable resources, such as organic fertilizers and biopesticides derived from agricultural by-products or waste, supports environmentally friendly agricultural practices (Copping, L. G; Menn, J. J., 2000). It is estimated that crop residues alone have the potential to provide a significant portion of the world’s energy needs (Scurlock, J; Hall, D. O., 1998).

The primary aim of this study is to utilize machine learning techniques to identify countries with similar agricultural characteristics that are trending towards underutilizing or overutilizing their agricultural contribution to GDP. The objective is to highlight nations that are nearing low or high dependence on agriculture concerning their respective GDPs. Understanding these trends and implementing suitable measures will enable governments and policymakers to guide their economies towards sustainable agricultural development, economic diversification, and improved resilience in the face of emerging challenges. Emphasizing the role of the agricultural sector in the broader economic context is pivotal for fostering stable and prosperous nations.

II.  Process

A.  Data Gathering

Data features were retrieved from FAO and an open-source GitHub repository. Data characteristics and sources can be found in the Data Sources section for each data table retrieved. The emphasis of the data gathering process revolved around the following categories:

Climate Change

Investment

Land, Inputs, and Sustainability

Population and Employment

Price

Production

Trade

Macro-Economic Indicators

B.  Data Cleaning

In this study, the data underwent a thorough cleaning process to ensure its quality and reliability. The data cleaning was based on assumptions provided by FAO and incorporated custom-made business rules tailored to the specific research objectives. The data cleaning process was crucial to address potential inconsistencies, errors, and missing values in the dataset. By rectifying these issues, we aimed to enhance the accuracy of the subsequent analysis and machine learning modeling.

Assumptions/Business Rules

  • China
  • United States of America
  • India
  • Ethiopia PDR
  • Serbia and Montenegro
  • USSR
  • Yugoslav SFR
  • Czechoslovakia
  • Netherlands Antilles (former)
  • Belgium-Luxembourg
  • Pacific Islands Trust Territory
  • Midway Island
  • Wake Island
  • Netherlands
  • Taiwan
  • Area: Countries Only
  • Element: Temperature Change (°C)
  • Item Group: Dec-Jan-Feb, Mar-Apr-May, Jun-Jul-Aug, Sep-Oct-Nov, Meteorological Year
  • Value: Multiplied “Temperature Change (°C)” by 1.8 to retrieve the change in degrees in relation to Fahrenheit
  • Area: Countries Only
  • Element: $US Dollar Amounts ($1,000,000 USD Current), Share of Total $US Dollar Amounts (%)
  • Item: Value Added (Agriculture, Forestry and Fishing), Gross Fixed Capital Formation (Agriculture, Forestry and Fishing), Agriculture, Forestry, Fishing (Central Government), Credit to Agriculture, Forestry and Fishing, DFA Disbursement to Agriculture, Forestry and Fishing, FDI inflows to Agriculture, Forestry and Fishing
  • Value: Multiplied “$US Dollar Amounts” by 1,000,000 to retrieve the actual value, divided “Share of Total $US Dollar Amounts” by 100 to retrieve % value
  • Area: Countries Only
  • Element: 1,000 Hectares
  • Item: Land Area, Agricultural Land, Cropland, Arable Land, Forest Land, Land Under Permanent Crops, Other Land, Inland Waters, Land Area Equipped for Irrigation, Land Area Actually Irrigated, Agriculture Area Actually Irrigated, Coastal Waters, Land Used for Aquaculture
  • Value: Multiplied “1,000 Hectares” by 1,000 to retrieve the actual value
  • Area: Countries Only
  • Element: Agricultural Use (Tonnes), Export Quantity (Tonnes), Import Quantity (Tonnes), Production (Tonnes)
  • Item: Nutrient Nitrogen (N), Nutrient Phosphate (P2O5), Nutrient Potash (K2O)
  • Value: Kept default value (“Tonnes”)
  • Area: Countries Only
  • Element: Amount Excreted in Manure (N Content) (Kg), Manure Applied to Soils (N Content) (Kg), Stocks (Head Count)
  • Item Group: Asses, Buffalo, Camels, Cattle, Chickens, Ducks, Goats, Horses, Llamas, Mules and Hinnies, Sheep, Swine, Turkeys
  • Value: Kept default value for each specific element (“Kg”, “Head Count”)
  • Area: Countries Only
  • Element: Agriculture Use (Tonnes)
  • Item Group: Disinfectants, Fungicides and Bactericides, Fungicides – Seed Treatments, Herbicides, Insecticides, Insecticides – Seed Treatments, Mineral Oils, Other Pesticides NES, Plant Growth Regulators, Rodenticides
  • Value: Kept default value (“Tonnes”)
  • Area: Countries Only
  • Source: Labour Force Survey
  • Indicator: Employment in Agriculture, Forestry and Fishing by Age 15 to 24 (1000 Persons), Employment in Agriculture, Forestry and Fishing by Age 25 to 54 (1000 Persons), Employment in Agriculture, Forestry and Fishing by Age 55 to 64 (1000 Persons), Employment in Agriculture, Forestry and Fishing by Age 65+, Mean Weekly Hours Actually Worked per Employed Person in Agriculture, Forestry and Fishing (Number), Mean Weekly Hours Actually Worked per Employee in Agriculture, Forestry and Fishing (Number), Share of Employment in Agriculture, Forestry and Fishing in Total Employment (%), Share of Employees in Agriculture, Forestry and Fishing in Total Employees (%)
  • Value: Multiplied “1000 Persons” by 1,000 to retrieve the actual value, kept default value for “Number”, divided “%” by 100 to retrieve % value
  • Area: Countries Only
  • Element: Total Population – Both Sexes (1000 Persons), Rural Population (1000 Persons), Urban Population (1000 Persons)
  • Value: Multiplied “1000 Persons” by 1,000 to retrieve the actual value
  • Area: Countries Only
  • Element: Producer Price ($USD/Tonne)
  • Item Group: Cereals, Eggs, Fibres, Fruits, Meat, Milk, Nuts, Oils, Other, Pulses, Roots and Tubers, Seeds, Spices, and Seasoning, Vegetables
  • Value: Kept default value (“$USD/Tonne”)
  • Area: Countries Only
  • Element: Area Harvested (Hectares), Production (Tonnes), Producing Animals/Slaughtered (Head), Producing Population (Number), Stocks (Head)
  • Item Group: Animals, Butter and Ghee, Cereals, Cheese, Eggs, Fibres, Fruits, Hides and Skins, Meat, Milk, Nuts, Oils, Other, Pulses, Roots and Tubers, Seeds, Spices, and Seasoning, Vegetables
  • Value: Kept default value for each specific element (“Hectares”, “Tonnes”, “Head”, “Number”, “Head”)
  • Area: Countries Only
  • Element: Gross Production Value ($1,000 USD Current)
  • Item Group: Cereals, Eggs, Fibres, Fruits, Meat, Milk, Nuts, Oils, Other, Pulses, Roots and Tubers, Seeds, Spices, and Seasoning, Vegetables
  • Value: Multiplied “Gross Production Value” by 1,000 to retrieve actual value
  • Area: Countries Only
  • Element: Import Quantity (Tonnes, Number, Head, 1000 Head), Import Value ($1,000 USD Current), Export Quantity (Tonnes, Number, Head, 1000 Head), Export Value ($1,000 USD Current)
  • Item Group: Animals, Beverages, Butter and Ghee, Cereals, Cheese, Eggs, Fibres, Fodder, Fruits, Hides and Skins, Meat, Milk, Nuts, Oils, Other, Pulses, Roots and Tubers, Seeds, Spices, and Seasoning, Vegetables
  • Value: Multiplied “Import Value” and “Export Value” by 1,000 to retrieve actual value, multiplied “Import Quantity (1000 Head)” and “Export Quantity (1000 Head)” by 1,000 to retrieve actual value and then merged with “Head” elements so there would only be one “Head” element, kept default for “Import Quantity (Tonnes, Number)” and “Export Quantity (Tonnes, Number)”
  • Area: Countries Only
  • Element: Value ($USD), Share of GDP (%), Annual Growth (%)
  • Item: Value Added (Agriculture, Forestry and Fishing)
  • Value: Multiplied “Value ($USD)” by 1,000,000 to retrieve the actual value, divided “Share of GDP (%)” and “Annual Growth (%)” by 100 to retrieve the actual value
  • 60 countries were removed
  • 57 features were removed
  • 61 features were removed

Feature Engineering

  • Weighting Factor = (1 – α) ⁿ

Figure 1: AWMA

  • Let’s say we have a time series data of closing stock prices for a company over the last 10 years:
  • Year 1: 100
  • Year 2: 110
  • Year 3: 120
  • Year 4: 130
  • Year 5: 140
  • Year 6: 150
  • Year 7: 160
  • Year 8: 170
  • Year 9: 180
  • Year 10: 190
  • To calculate the AWMA with a α of 0.1, first assign a weighting factor to each data point based on the α value Where n is the number of years ago the data point is (0 for most recent, 9 for oldest):
  • Year 1: (1 – 0.1) ⁰ = 1
  • Year 2: (1 – 0.1) ¹ = 0.9
  • Year 3: (1 – 0.1) ² = 0.81
  • Year 4: (1 – 0.1) ³ = 0.729
  • Year 5: (1 – 0.1) ⁴ = 0.6561
  • Year 6: (1 – 0.1) ⁵ = 0.59049
  • Year 7: (1 – 0.1) ⁶ = 0.531441
  • Year 8: (1 – 0.1) ⁷ = 0.4782969
  • Year 9: (1 – 0.1) ⁸ = 0.43046721
  • Year 10: (1 – 0.1) ⁹ = 0.387420489
  • Next, multiply each data point by its corresponding weighting factor and sum the results. The AWMA with an alpha of 0.1 would be 143.88 for this dataset.
  • 143.88 = (100*1) + (110*0.9) + (120*0.81) + (130*0.729) + (140*0.6561) + (150*0.59049) + (160*0.531441) + (170*0.4782969) + (180*0.43046721) + (190*0.387420489) / (1 + 0.9 + 0.81 + 0.729 + 0.6561 + 0.59049 + 0.531441 + 0.4782969 + 0.43046721 + 0.387420489)
  • X (Scaled) = (X – X(Minimum)) / (X(Maximum) – X(Minimum))
  • Let’s say X1 has values [1, 2, 3, 4] and X2 has values [10, 20, 30, 40]. The Min-Max Scaler formula will be applied as follows:
  • X1 (Scaled) = (X1 – 1) / (4 – 1) = [0, 0.33, 0.67, 1]
  • X2 (Scaled) = (X2 – 10) / (40 – 10) = [0, 0.33, 0.67, 1]
  • Suppose we have a dataset with 4 features X1, X2, X3, and X4 and we want to reduce the dimensionality of the dataset to 2 principal components. The steps involved in calculating PCA are as follows:
  • Compute the covariance matrix: The covariance matrix is a square matrix that describes the covariance between each pair of features in the dataset.
  • Compute the eigenvectors and eigenvalues of the covariance matrix: The eigenvectors are the directions along which the variance of the data is maximized, and the eigenvalues represent the amount of variance along each eigenvector.
  • Choose the top k eigenvectors: The top k eigenvectors correspond to the k most important principal components and are used to form the new feature space.
  • Transform the original features into the new feature space: The original features are transformed into the new feature space by multiplying them with the top k eigenvectors.
  • Suppose our 4 features X1, X2, X3, and X4 have the following covariance matrix:
  • X1 [1.0 0.5 0.2 0.3]
  • X2 [0.5 1.0 0.4 0.2]
  • X3 [0.2 0.4 1.0 0.1]
  • X4 [0.3 0.2 0.1 1.0]
  • After computing the eigenvectors and eigenvalues of the covariance matrix, we get 2 eigenvectors (v1 and v2) that correspond to the 2 largest eigenvalues. These 2 eigenvectors (v1 and v2) will be used to form the new feature space. Finally, we transform the original features into the new feature space by multiplying them with the 2 eigenvectors. The transformed features (PC1 and PC2) will be the 2 principal components.

C.  Data Modeling

With the completion of the data gathering and meticulous cleaning processes, the subsequent pivotal step in our study was model selection. This crucial phase involved the careful evaluation and comparison of various machine learning algorithms to identify the most suitable one that aligned with the specific problem and dataset. The objective of model selection was to find a model that could effectively capture the underlying patterns and relationships present in the data. Each machine learning algorithm comes with its own strengths and weaknesses, making it essential to choose the right model that would yield accurate and meaningful results for our research. By meticulously considering the characteristics of the dataset and the objectives of the study, we ensured that the chosen model was well-suited to our research, allowing us to draw meaningful conclusions and provide valuable recommendations for policymakers and stakeholders alike.

Model Selection

  • Initialization: The first step is to initialize the centroids of the clusters. This can be done randomly, by selecting k data points from the dataset as the initial centroids, or by using a more sophisticated method such as the K-Means Clustering algorithm.
  • Assignment Step: In this step, each data point is assigned to the closest centroid based on its Euclidean distance from the centroids.
  • Recalculation Step: The centroids are recalculated as the mean of all the data points assigned to the corresponding cluster.
  • Repeat Steps 2 and 3: The assignment and recalculation steps are repeated until the centroids no longer change, or a maximum number of iterations has been reached.
  • Final Clusters: The final clusters consist of the data points that have been assigned to the same centroid in the final iteration.
  • Evaluation: The final clusters can be evaluated by calculating the sum of squared distances (SSE) between the data points and their respective centroids. This can be used to determine the optimal number of clusters.
  • The Elbow Method is used to determine the optimal number of clusters for a given dataset. The idea behind the Elbow Method is to plot the value of the within-cluster sum of squares (WCSS) against the number of clusters and choose the number of clusters at the “elbow” point, which is the point of diminishing returns. The WCSS value measures the total distance between each data point and the centroid of its assigned cluster. The Elbow Method assumes that as the number of clusters increases, the WCSS will decrease, but at a decreasing rate. The “elbow” point is the value of k at which adding another cluster provides little or no benefit in reducing the WCSS.
  • The Silhouette Score is a measure of the similarity between each data point and the other data points in the same cluster, compared to the other clusters. The Silhouette Score ranges from -1 to 1, with a value close to 1 indicating a high degree of similarity within the same cluster and a low degree of similarity between the data points and other clusters, and a value close to -1 indicating a low degree of similarity within the same cluster and a high degree of similarity between the data points and other clusters. The Silhouette Score is used to evaluate the quality of the clustering solution, with higher values indicating a better clustering solution.
  • Initialization: The first step is to initialize the model with a simple predictor, such as a mean or median.
  • Boosting: In each iteration, the algorithm fits a weak learner to the negative gradient of the loss function, which is used to evaluate the model’s performance. The weak learner is then added to the existing model to make a new, stronger prediction model.
  • Gradient Calculation: The gradient of the loss function is calculated using the gradient tree boosting technique, which involves fitting decision trees to the residuals of the model. The trees are grown using the gradient descent algorithm to minimize the loss function.
  • Prediction: The final model is used to make predictions by combining the predictions of all the weak predictors.
  • Regularization: To avoid overfitting, XGBoost uses a regularization term called the L1 or L2 regularization term, which penalizes complex models. This helps to reduce the variance of the model and prevent overfitting.
  • Start with a baseline value: The baseline value represents the expected prediction of the model when all features have their average values. This value can be seen as the overall contribution of all features to the prediction.
  • Calculate the contribution of each feature: The contribution of each feature to the prediction is calculated by comparing the prediction of the model when a specific feature takes its actual value to the prediction when that feature takes its baseline value. The difference between these two values is the contribution of the feature.
  • Account for the interaction between features: The interaction between features is considered by considering all possible combinations of features and their values. This process helps to determine the contribution of each feature to the prediction, considering how it interacts with other features.
  • Combine the contributions to get the SHAP value: The contributions of all features are combined to get the final SHAP value for each prediction. The SHAP value represents the contribution of each feature to the prediction, and its magnitude and sign indicate whether the feature increases or decreases the prediction.

Model Hyperparameter Tuning

  • Elbow Method: The result of the Elbow Method is shown below, the optimal number of clusters identified was 5.

Figure 2: Elbow Method

  • Silhouette Score: The Silhouette Score analysis revealed that the optimal number of clusters for the given data was 2 and 5. However, it was determined that using only 2 clusters would not provide sufficient differentiation for the data. Therefore, the optimal number of clusters identified through the Elbow Method, which was 5, was chosen instead.

Figure 3: Silhouette Score

D.  Model Results

Following the model selection process, the subsequent crucial step entailed interpreting the results obtained from the chosen machine learning algorithm. This phase involved a comprehensive analysis of countries based on their respective clusters and agriculture GDP rankings. Additionally, we grouped the countries based on whether they were identified as underutilizing or overutilizing their agricultural contribution to GDP. The model’s output provided valuable insights into the different patterns and relationships within the dataset. By clustering countries with similar agricultural characteristics, we gained a deeper understanding of regional trends and commonalities among nations. This allowed us to identify clusters of countries facing similar challenges or opportunities in their agricultural sectors. Moreover, the agriculture GDP rankings provided an ordered perspective, highlighting which countries held significant agricultural contributions to their overall GDP and which ones had relatively lower dependence on the agricultural sector. Understanding these rankings enabled us to assess the economic significance of agriculture in each country’s development.

Cluster Results

  • Australia
  • Brazil
  • Canada
  • Indonesia
  • Iran
  • Mexico
  • Nigeria
  • Pakistan
  • Russian Federation
  • Turkey
  • Vietnam
  • Afghanistan
  • Albania
  • Algeria
  • Argentina
  • Armenia
  • Azerbaijan
  • Bangladesh
  • Barbados
  • Belarus
  • Belize
  • Bhutan
  • Bolivia
  • Bosnia and Herzegovina
  • Botswana
  • Burkina Faso
  • Cabo Verde
  • Cambodia
  • Chile
  • Colombia
  • Comoros
  • Congo
  • Cook Islands
  • Costa Rica
  • Croatia
  • Cuba
  • Côte d’Ivoire
  • Democratic Republic of the Congo
  • Dominican Republic
  • Ecuador
  • Egypt
  • El Salvador
  • Eswatini
  • Ethiopia
  • Fiji
  • Gambia
  • Georgia
  • Ghana
  • Guatemala
  • Guyana
  • Jamaica
  • Jordan
  • Kazakhstan
  • Kenya
  • Kyrgyzstan
  • Laos
  • Lebanon
  • Lesotho
  • Liberia
  • Madagascar
  • Malawi
  • Malaysia
  • Mali
  • Mauritania
  • Mauritius
  • Mongolia
  • Montenegro
  • Morocco
  • Myanmar
  • Namibia
  • Nepal
  • Nicaragua
  • Niger
  • North Macedonia
  • Oman
  • Palestine
  • Panama
  • Peru
  • Philippines
  • Republic of Moldova
  • Rwanda
  • Saint Lucia
  • Samoa
  • Senegal
  • Serbia
  • Seychelles
  • Sierra Leone
  • Somalia
  • South Africa
  • Sri Lanka
  • Sudan
  • Syrian Arab Republic
  • Tajikistan
  • Thailand
  • Timor-Leste
  • Togo
  • Tonga
  • Trinidad and Tobago
  • Tunisia
  • Uganda
  • Ukraine
  • United Republic of Tanzania
  • Uruguay
  • Venezuela
  • Yemen
  • Zambia
  • France
  • Germany
  • Italy
  • Spain
  • Austria
  • Bahamas
  • Belgium
  • Brunei Darussalam
  • Bulgaria
  • Cyprus
  • Czechia
  • Denmark
  • Estonia
  • Finland
  • Greece
  • Hungary
  • Iceland
  • Ireland
  • Israel
  • Japan
  • Kuwait
  • Latvia
  • Lithuania
  • Luxembourg
  • Malta
  • New Caledonia
  • New Zealand
  • Norway
  • Poland
  • Portugal
  • Qatar
  • Republic of Korea
  • Romania
  • Singapore
  • Slovakia
  • Slovenia
  • Sweden
  • Switzerland
  • United Arab Emirates
  • Angola
  • Antigua and Barbuda
  • Bahrain
  • Benin
  • Burundi
  • Cameroon
  • Central African Republic
  • Chad
  • Democratic People’s Republic of Korea
  • Dominica
  • Eritrea
  • French Polynesia
  • Gabon
  • Grenada
  • Guinea
  • Guinea-Bissau
  • Haiti
  • Honduras
  • Iraq
  • Libya
  • Maldives
  • Mozambique
  • Papua New Guinea
  • Paraguay
  • Saint Kitts and Nevis
  • Saint Vincent and the Grenadines
  • Sao Tome and Principe
  • Saudi Arabia
  • Suriname
  • Turkmenistan
  • Uzbekistan
  • Share of Employees in Agriculture, Forestry and Fishing (%): 18.17%
  • DFA Disbursement to Agriculture, Forestry and Fishing Value ($USD): 17.07%
  • Nuts Import Value ($USD): 7.32%
  • Share of Employment in Agriculture, Forestry and Fishing (%): 6.49%
  • Beverages Export Quantity (Tonnes): 5.92%
  • Investment Value Added in Agriculture, Forestry, and Fishing ($USD): 5.35%
  • Vegetables Production Quantity (Tonnes): 4.16%
  • Dec-Jan-Feb Temperature Change (°F): 3.25%
  • Share of DFA Disbursement to Agriculture, Forestry, and Fishing (%): 3.03%
  • Nutrient Nitrogen N Import Quantity (Tonnes): 3.03%
  • Share of Employees in Agriculture, Forestry and Fishing (%): 2.28
  • DFA Disbursement to Agriculture Forestry and Fishing Value ($USD): 1.98
  • Share of Employment in Agriculture, Forestry and Fishing (%): 0.58
  • Share of DFA Disbursement to Agriculture, Forestry, and Fishing (%): 0.54
  • Investment Value Added in Agriculture, Forestry, and Fishing ($USD): 0.45
  • Nuts Import Value ($USD): 0.30
  • Vegetables Production Quantity (Tonnes): 0.22
  • Milk Import Value ($USD): 0.15
  • Fruit Price Value ($USD/Tonne): 0.12
  • Nutrient Nitrogen N Import Quantity (Tonnes): 0.11

Analysis

  • Cluster 0
  • Accounts for 6.15% (11) of all countries of which 45.45% (5) are in Asia
  • Cluster 1
  • Accounts for 53.63% (96) of all countries
  • 72.55% (37) of all African countries are in Cluster 1
  • 54.35% (25) of all Asian countries are in Cluster 1
  • Cluster 2
  • Accounts for 2.79% (5) of all countries of which 100.00% (5) are in Europe
  • Cluster 3
  • Accounts for 19.55% (35) of all countries of which 65.71% (23) are in Europe
  • 60.53% (23) of all European countries in Cluster 3
  • Cluster 4
  • Accounts for 17.88% (32) of all countries of which 40.63% (13) are in Africa
  • 25.49% (13) of all African countries are in Cluster 4
  • 31.82% (7) of all North American countries are in Cluster 4

After analyzing the distribution, we proceeded to incorporate GDP data to further compare the clusters. The dashboard visualization titled “Average GDP Value in Agriculture ($USD) vs Average GDP Share of Agriculture (%), based on Cluster” illustrates the distinctions among the clusters in terms of their average agriculture GDP value and share. The size of the circles represents the distribution (count) of countries within each cluster. Upon examination, we observed that Clusters 0 and 2 have low counts but remarkably high agriculture GDP values. Clusters 3 and 4 exhibit similar counts and agriculture GDP values, but they differ significantly in terms of their agriculture GDP share. Cluster 1, with the largest count, also displays a high agriculture GDP share. To delve deeper on a country level, we utilized the “GDP Value in Agriculture ($USD) vs GDP Share of Agriculture (%), based on Cluster” visualization. This visualization showcases the countries, their respective clusters, and a color-coded quadrant. It is essential to note that the quadrant split was determined based on the average agriculture GDP value ($8,642,233,978) and average agriculture GDP share (12.60%) of all countries. The quadrant labels used in this visualization are independent of the GDP Ranking labels and were solely employed to indicate the countries’ positions relative to the average values. Upon analyzing the visualization, we noticed that Cluster 0 comprises countries with high agriculture GDP values but exhibits variation in terms of their agriculture GDP shares. This implies that some countries within the cluster are more dependent on agriculture relative to their GDP compared to others. For instance, Pakistan and Russia have similar agriculture GDP values ($53,834,716,269.41 and $53,677,018,322.42, respectively), yet their agriculture GDP shares vary significantly (22.97% and 3.93%, respectively). Most countries in Clusters 1 and 4 mainly fall into the low agriculture GDP value zone, but they display variations when considering agriculture GDP share. All countries in Cluster 2 are situated in the high agriculture GDP value and low agriculture GDP share zone, indicating that these are developed countries. Cluster 3 consists of countries with low agriculture GDP values and low agriculture GDP shares, signifying that these countries make minimal contributions to their GDP through agriculture and are not heavily dependent on it. Overall, the addition of GDP data provided valuable insights into the clusters, allowing us to identify variations in agriculture GDP value, share, and dependence on agriculture across different countries and clusters.

  • High GDP Value, High GDP Share
  • Accounts for 10.06% (18) of all countries of which 55.56% (10) are in Africa and 44.44% (8) are in Asia
  • 66.67% (12) of High GDP Value, High GDP Share countries are in Cluster 1
  • High GDP Value, Medium GDP Share
  • Accounts for 12.29% (22) of all countries of which 36.36% (8) are in Asia and 27.27% (6) are in South America
  • 50.00% (6) of all South American countries are in High GDP Value, Medium GDP Share
  • 68.18% (15) of High GDP Value, Medium GDP Share countries are in in Cluster 1
  • High GDP Value, Low GDP Share
  • Accounts for 11.17% (20) of all countries of which 60.00% (12) are in Europe
  • 100% (5) of Cluster 2 countries in in High GDP Value, Low GDP Share
  • 40.00% (8) of High GDP Value, Low GDP Share countries are in Cluster 3
  • Medium GDP Value, High GDP Share
  • Accounts for 15.08% (27) of all countries of which 55.56% (15) are in Africa
  • 29.41% (15) of all African countries and 17.39% (8) of all Asian countries are in Medium GDP Value, High GDP Share
  • 74.07% (20) of Medium GDP Value, High GDP Share countries are in Cluster 1
  • 25.93% (7) of Medium GDP Value, High GDP Share countries are in Cluster 4
  • Medium GDP Value, Medium GDP Share
  • Accounts for 8.38% (15) of all countries of which 26.67% (4) are in North America
  • 73.33% (11) of Medium GDP Value, High GDP Share countries are in Cluster 1
  • Medium GDP Value, Low GDP Share
  • Accounts for 9.50% (17) of all countries of which 58.82% (10) are in Europe
  • 26.32% (10) of all European countries are in Medium GDP Value, Low GDP Share
  • 64.71% (11) of Medium GDP Value, Low GDP Share countries are in Cluster 3
  • Low GDP Value, High GDP Share
  • Accounts for 8.38% (15) of all countries of which 53.33% (8) are in Africa
  • 15.69% (8) of all African countries are in Low GDP Value, High GDP Share
  • 53.33% (8) of Low GDP Value, High GDP Share countries are in Cluster 1
  • 46.67% (7) of Low GDP Value, High GDP Share countries are in Cluster 4
  • Low GDP Value, Medium GDP Share
  • Accounts for 12.29% (22) of all countries of which 31.82% (7) are in Africa
  • 72.73% (16) of Low GDP Value, Medium GDP Share countries are in Cluster 1
  • Low GDP Value, Low GDP Share
  • Accounts for 12.85% (23) of all countries of which 26.09% (6) are in Africa and 26.06% (6) are in North America
  • 52.17% (12) of Low GDP Value, Low GDP Share countries are in Cluster 3

After comprehending the segmented distribution of GDP Ranking and clusters, the subsequent step involved comparing them. The dashboard visualization titled “Average GDP Value in Agriculture ($USD) vs Average GDP Share of Agriculture (%), based on Cluster and GDP Ranking” demonstrates the distinctions among clusters and GDP rankings concerning their average agriculture GDP value and share. The size of the circles represents the counts of countries within each GDP ranking category. Upon analysis, we observed that Clusters 0 and 2 have low counts but exhibit high agriculture GDP values, as discussed earlier. Clusters 1 and 4 display similar counts and encompass a mixture of GDP ranking categories. These clusters indicate the presence of countries with similar characteristics yet varying agriculture GDP features. Cluster 3 showcases average counts, but with low agriculture GDP values and shares. The subsequent step involved drilling down to the country level, similar to the “GDP Value in Agriculture ($USD) vs GDP Share of Agriculture (%), based on Cluster” visualization, but this time based on GDP Ranking rather than clusters. The dashboard visualization titled “GDP Value in Agriculture ($USD) vs GDP Share of Agriculture (%), based on GDP Ranking” showcases the countries, their corresponding GDP Rankings, and utilizes the same color quadrant employed in the cluster analysis. We observed that the quantiles effectively segmented the countries based on the previously established quadrants. Furthermore, the dashboard visualization titled “GDP Value in Agriculture ($USD) vs GDP Share of Agriculture (%), based on Cluster and GDP Ranking” presents the countries based on GDP Ranking, segmented by their respective clusters. Clusters 0 and 2 consist solely of countries with high GDP values. Clusters 1 and 4 encompass a diverse mixture of various GDP Ranking categories. Notably, all five countries in Cluster 2 are developed countries with high agriculture GDP values and low agriculture GDP shares. In summary, these visualizations allow for a comprehensive examination of the relationships between agriculture GDP value, agriculture GDP share, clusters, and GDP rankings. They provide valuable insights into the distribution and characteristics of countries within the different segments, aiding in the understanding of variations in agricultural contribution to GDP across different clusters and GDP rankings.

  • Cluster 0
  • High GDP Value, Low GDP Share: Russia, Mexico, Australia, and Canada. GDP Values ranged from ~$27.5 billion to ~$53.6 billion and GDP Share ranged from 1.89% to 3.93%
  • Russia had the highest GDP Value and GDP Share at $53,677,018,322.42 and 3.93%
  • Canada had the lowest GDP Value and GDP Share at $27,543,299,398.29 and 1.89%
  • Within this cluster, Canada has the most potential for growth in relation to both their GDP Value and GDP Share. Since all of these countries have similar agricultural characteristics, Canada, Australia, and Mexico should invest more into their agricultural sectors to meet their agricultural potential (Russia)
  • Cluster 1
  • High GDP Value, Low GDP Share: South Africa, and Chile. GDP Values ranged from ~$7.8 billion to ~$8.5 billion and GDP Share ranged from 2.46% to 3.98%
  • Medium GDP Value, Low GDP Share: Oman, Croatia, Panama, Lebanon, and Cuba. GDP Values ranged from ~$1.2 billion to ~$2.9 billion and GDP Share ranged from 1.85% to 4.18%
  • Low GDP Value, Low GDP Share: Trinidad and Tobago, Barbados, Botswana, Seychelles, Saint Lucia, Cook Islands, and Mauritius. GDP Values ranged from ~$8 million to ~$366 million and GDP Share ranged from 1.21% to 4.03%
  • Cluster 2
  • High GDP Value, Low GDP Share: United Kingdom of Great Britain and Northern Ireland, Germany, France, Italy, and Spain. GDP Values ranged from ~$17.1 billion to ~$40.5 billion and GDP Share ranged from 0.70% to 2.88%
  • France had the highest GDP Value at $40,451,583,923.77
  • United Kingdom of Great Britain and Northern Ireland had the lowest GDP Share at 0.70%
  • Within this cluster, United Kingdom of Great Britain and Northern Ireland and Germany have the most potential for growth in relation to both their GDP Value and GDP Share. United Kingdom of Great Britain and Northern Ireland and Germany should invest more into their agricultural sectors to meet their agricultural potential (France). Italy and Spain are on the relative higher end in regard to GDP Share yet should still invest into the agricultural sectors as well, just not as heavily as the United Kingdom of Great Britain and Northern Ireland and Germany
  • Cluster 3
  • High GDP Value, Low GDP Share: Japan, Republic of Korea, Sweden, Norway, Poland, Finland, Greece, and Hungary. GDP Values ranged from ~$4.9 billion to ~$56.2 billion and GDP Share ranged from 1.20% to 4.18%
  • Medium GDP Value, Low GDP Share: Switzerland, Belgium, United Arab Emirates, Israel, Austria, Denmark, Ireland, Czechia, Slovakia, Portugal, and Lithuania. GDP Values ranged from ~$1.5 billion to ~$4.8 billion and GDP Share ranged from 0.77% to 3.93%
  • Low GDP Value, Low GDP Share: Singapore, Qatar, Luxembourg, Kuwait, Bahamas, Brunei Darussalam, Malta, New Caledonia, Slovenia, Cyprus, Estonia, and Latvia. GDP Values ranged from ~$90.7 million to ~$1.1 billion and GDP Share ranged from 0.08% to 4.08%
  • Cluster 4
  • High GDP Value, Low GDP Share: Saudi Arabia. GDP Value was ~$15 billion and GDP Share was 2.82%
  • Medium GDP Value, Low GDP Share: Libya. GDP Value was ~$1.9 billion and GDP Share was 3.77%
  • Low GDP Value, Low GDP Share: Bahrain, Saint Kitts and Nevis, Antigua and Barbuda, and French Polynesia. GDP Values ranged from ~$9.4 million to ~$170 million and GDP Share ranged from 0.36% to 3.13%
  • Cluster 0
  • High GDP Value, High GDP Share: Indonesia, Nigeria, Pakistan, and Vietnam. GDP Values ranged from ~$28.3 billion to ~$107.4 billion and GDP Share ranged from 13.61% to 22.97%
  • Indonesia had the highest GDP Value and lowest GDP Share at $107,436,478,883.95 and 13.61%
  • Vietnam had the lowest GDP Value at $28,328,324,531.15
  • Within this cluster, Indonesia is the least dependent on agricultural in regard to their GDP yet still highly dependent when compared to the countries in the same cluster with “High GDP Value, Low GDP Share”. The maximum agriculture GDP Share for “High GDP Value, Low GDP Share” countries was 3.93% (Russia) compared to 22.97% (Pakistan) for “High GDP Value, High GDP Share” countries. Ironically, they both have similar GDP Values of ~$53 billion which shows how dependent one country is compared to the other
  • Cluster 1
  • High GDP Value, High GDP Share: Bangladesh, Kenya, Côte d’Ivoire, Syrian Arab Republic, Ghana, Democratic Republic of the Congo, United Republic of Tanzania, Uganda, Nepal, Myanmar, Sudan, and Ethiopia. GDP Values ranged from ~$5.6 billion to ~$29.7 billion and GDP Share ranged from 15.92% to 38.86%
  • Medium GDP Value, High GDP Share: Mongolia, Senegal, Yemen, Nicaragua, Armenia, Mauritania, Tajikistan, Laos, Albania, Burkina Faso, Rwanda, Malawi, Madagascar, Cambodia, Afghanistan, Mali, Niger, Somalia, Sierra Leone, and Liberia. GDP Values ranged from ~$1.2 billion to ~$4.6 billion and GDP Share ranged from 13.64% to 63.18%
  • Low GDP Value, High GDP Share: Kyrgyzstan, Togo, Guyana, Tonga, Bhutan, Timor-Leste, Guyana, Gambia, and Comoros. GDP Values ranged from ~$69 million to ~$1.1 billion and GDP Share ranged from 18.03% to 32.86%
  • Cluster 4
  • High GDP Value, High GDP Share: Cameroon and Uzbekistan. GDP Values ranged from ~$5.3 billion to ~15.6 billion and GDP Share ranged from 17.03% to 30.14%
  • Medium GDP Value, High GDP Share: Papa New Guinea, Haiti, Guinea, Democratic People’s Republic of Democratic People’s Republic of Korea, Mozambique, Benin, and Chad. GDP Values ranged from ~$2 billion to ~4.1 billion and GDP Share ranged from 19.03% to 33.24%
  • Low GDP Value, High GDP Share: Dominica, Sao Tome and Principe, Eritrea, Vanuatu, Central African Republic, Burundi, and Guinea-Bissau. GDP Values ranged from ~$40.1 million to ~$914.9 million and GDP Share ranged from 13.57% to 40.64%

III.  Conclusion

A.  Recommendations

Agriculture plays a vital role in a country’s economy and overall development. However, both underutilization and overutilization of agriculture GDP can have significant implications for a country’s growth and potential. Underutilization of agriculture GDP implies that the agricultural sector is not effectively contributing to the country’s overall GDP. In such cases, investing in agricultural technologies can be beneficial. Technologies such as precision farming, smart irrigation systems, agricultural robotics, genetic engineering, and remote sensing can enhance productivity, sustainability, and profitability within the agricultural sector. These advancements can help optimize resource utilization, improve crop yields, and streamline supply chains. On the other hand, overutilization of agriculture GDP, which can be characterized as having high dependence on agriculture, may lead to risks and vulnerabilities in the economy. It is crucial for countries in this category to diversify into other industries to reduce reliance on agriculture. Industries such as manufacturing, technology, services, tourism, and renewable energy can offer opportunities for economic growth and resilience. Balancing the utilization of agriculture GDP is essential for a country’s long-term economic growth and sustainability. Governments and policymakers should focus on promoting a diversified economy while investing in innovative agricultural technologies to unlock the full potential of the agricultural sector and drive overall economic development.

Our recommendations are based on specific rules and assumptions that we followed. These rules were established to identify areas with high potential for underutilization or overutilization of their agricultural GDP. The selection process considered various dimensions, and we focused on clusters that included countries representing all three GDP ranking categories in relation to their agricultural utilization.

Clusters 1, 3, and 4 were selected for underutilization potential because the countries in Clusters 0 and 2 only consisted of countries classified as “High GDP Value, Low GDP Share.” This indicated that these countries were at the top tier in terms of their GDP within their respective clusters. Since Clusters 1, 3, and 4 had countries representing all three GDP ranking categories, it was straightforward to recommend countries in the “Medium GDP Value, Low GDP Share” and “Low GDP Value, Low GDP Share” categories. However, for this analysis, we specifically focused on the countries in the “Medium GDP Value, Low GDP Share” category. This approach was chosen because the countries in the “Low GDP Value, Low GDP Share” category had extremely low agriculture GDP values compared to the other GDP ranking categories. The agriculture GDP values ranged from approximately $8 million (Cook Islands in Cluster 1) to around $1.1 billion (Latvia in Cluster 3). Even the lowest agriculture GDP value in the “Medium GDP Value, Low GDP Share” category, surpassed the maximum agriculture GDP value in the “Low GDP Value, Low GDP Share” category (Latvia). This stark discrepancy led us to focus on the countries in the “Medium GDP Value, Low GDP Share” category, as it showcased the notable disparity.

Clusters 0, 1, and 4 were the only clusters that met the established GDP ranking criteria for overutilization potential. Clusters 1 and 4 were chosen because Cluster 0 consisted solely of countries classified as “High GDP Value, High GDP Share.” These countries were heavily reliant on agriculture for their GDP but inversely exhibited top-tier agriculture GDP values. While these countries should consider diversifying into other industries to sustain economic growth, their current investments in the agriculture sector are yielding positive results. Since Clusters 1 and 4 encompassed countries representing all three GDP ranking categories, we applied the same logic from underutilization analysis above. Consequently, we disregarded countries classified as “Low GDP Value, High GDP Share” and focused on those classified as “Medium GDP Value, High GDP Share.” The rationale behind this approach was that “Low GDP Value, High GDP Share” countries had already achieved an optimal level of dependence on agriculture. The objective of the study was to identify countries with the potential for overutilization and underutilization. Hence, we recommended the “Medium GDP Value, High GDP Share” countries due to their potential risk of becoming overly dependent on agriculture without reaping the associated benefits.

Table 1 presents the countries with the potential for underutilization and overutilization in terms of their agriculture GDP. These countries are recommended to consider specific actions such as increasing investments in the agriculture sector (for potential underutilized countries), diversifying into other industries (for potential overutilized countries), or optimizing agriculture practices (for potential overutilized countries). It is noteworthy that Clusters 1 and 4 include countries in both utilization categories, suggesting a rationale behind their overutilization of agriculture GDP, likely associated with efficiency. Efficiency in agriculture refers to the ability to produce more output, such as crops or livestock, using fewer resources. This can be achieved through various means, including technological advancements, improved farming practices, better irrigation systems, and enhanced use of fertilizers and pesticides. While increased efficiency is generally desirable as it can boost productivity and reduce costs, it can also lead to overutilization and potential drawbacks for the agriculture GDP. When agricultural efficiency improves, farmers can produce larger quantities of crops or livestock on the same amount of land. This increased output often leads to a surplus of agricultural products in the market, which can drive down prices. As a result, farmers may feel compelled to produce even more to maintain their income levels or profit margins. This overproduction can create a situation of oversupply, where the market becomes saturated with agricultural goods. Overutilization occurs when farmers continue to intensify their production efforts, even when the market demand cannot absorb the surplus. This can lead to a downward spiral in prices, negatively impacting the agriculture GDP. Lower prices make it difficult for farmers to generate sufficient revenue, which can ultimately result in financial struggles, reduced investment in the sector, and even farm closures. Additionally, overutilization can have environmental implications. To increase output, farmers may resort to unsustainable practices, such as excessive use of fertilizers and pesticides. This can lead to soil degradation, water pollution, and damage to ecosystems. These environmental consequences can further affect agricultural productivity and sustainability in the long run. To avoid overutilization and its potential negative impacts, it is crucial to strike a balance between efficiency and market demand. Agricultural policies and regulations play a vital role in managing production levels, ensuring fair prices, and promoting sustainable practices. It’s important to consider the long-term viability of agricultural systems, considering factors like market dynamics, environmental sustainability, and the well-being of farmers and rural communities.

Table 1: Country Recommendations

IV.  Data Sources

A.  Food and Agriculture Organization of the United Nations

Climate Change: Temperature Change

  • Celsius Degrees

Investment: Country Investment Statistics Profile

  • The share of total flow allocated to agriculture (for Government Expenditure on Agriculture, Credit to Agriculture, Development Flows, Foreign direct Investment to Agriculture).
  • The agriculture shares of total GDP.
  • The agriculture shares of total gross fixed capital formation.
  • The agriculture orientation index (ratio of the agriculture share of total flow, over the agriculture value added share of total GDP) for Government Expenditure on Agriculture, Credit to Agriculture, Development Flows to Agriculture.
  • The investment agriculture orientation index (which is the ratio between the agriculture share of gross fixed capital formation over the agriculture share of GDP).
  • The annual growth.
  • The investment ratio (ratio between gross fixed capital formation over GDP).
  • The agriculture investment ratio (ratio between agriculture gross fixed capital formation and agriculture value added).
  • Government Expenditure: Defined in the Government Finance Statistics framework, equals Expense plus Net Investment in Non-financial Assets, and are grouped according to the COFOG categories. Expense are those transactions that imply a decrease of the net worth. Net acquisition of non-financial assets, instead, are those transactions that affect the stock of non-financial assets without changing the net worth.
  • Credit to Agriculture: Refers to the amount of loans provided by the private/commercial banking sector to producers in agriculture, forestry and fisheries, including household producers, cooperatives, and agro-businesses.
  • Official Development Assistance: Those flows to countries and territories which are provided by official agencies, including state and local governments, or by their executive agencies; and each transaction of which: is administered with the promotion of the economic development and welfare of developing countries as its main objective; and is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent).”
  • Foreign Direct Investment: An investment which aims to acquire a lasting management influence (10 percent or more of the voting stock) in an enterprise operating in a foreign economy. FDI involves both the initial transaction between the two entities and all subsequent transactions between them and among affiliated enterprises, both incorporated and unincorporated. FDI may be undertaken by individuals, as well as business entities. The foreign direct investor most often is aiming to gain access to natural resources, to markets, to labour supply, to technology, to ensure security of supplies or to control the quality of a certain product.
  • Value Added: Represents the contribution of labour and capital to the production process. Gross value added at basic prices is defined as output valued at basic prices less intermediate consumption valued at purchasers’ prices. Although the outputs and inputs are valued using different sets of prices, for brevity the value added is described by the prices used to value the outputs. From the point of view of the producer, purchasers’ prices for inputs and basic prices for outputs represent the prices actually paid and received. Their use leads to a measure of gross value added that is particularly relevant for the producer. Net value added is defined as the value of output less the values of both intermediate consumption and consumption of fixed capital.
  • Gross Fixed Capital Formation (GFCF): The total value of a producer’s acquisitions, less disposals, of fixed assets during the accounting period plus certain additions to the value of non-produced assets (such as subsoil assets or major improvements in the quantity, quality or productivity of land) realized by the productive activity of institutional units.
  • Government Expenditure on Agriculture: The statistical units are central government units, which are the institutional unit that carry out the function of government as their primary activity, excluding government business enterprises.
  • Credit to Agriculture: The private/commercial financial institutions.
  • Development Flows: The recipient countries.
  • Foreign Direct Investment: The agricultural holding; state owners and other public owners of forests, wood and paper products enterprises; catches by the individual fishing vessels and the production by the aquaculture enterprises.
  • Agriculture Value Added: The countries and territories.
  • Government Expenditure on Agriculture: The target population are central government units, excluding government business enterprise.
  • Credit to Agriculture: The universe of all private, commercial financial institutions that provide loans to households and business enterprises that produce goods and services for profit, and in particular, provide loans to agricultural holdings, owners of forest and producers of raw wood and primary wood and paper products, and fishing vessels and fisheries and aquaculture enterprises.
  • Development Flows: The universe of all countries, international organizations, and private sector foundations that provide development assistance to countries, particularly for purposes related to agriculture, forestry, fisheries, rural development, environmental protection, and food security.
  • Foreign Direct Investment: The universe of all institutional unit’s resident in the country; the agricultural holdings; all owners of forest and producers of raw wood and primary wood and paper products; and all fishing vessels and aquaculture enterprises.
  • Agriculture Value Added and Gross Fixed Capital Formation: The concept of statistical population is not applicable.
  • US dollar Current prices expressed in millions.
  • US dollar 2010 constant prices expressed in million.
  • Percentage (%) (agriculture share of total).
  • Index (agriculture orientation index).
  • Growth (annual, both in US dollar current prices and US dollars 2010 constant prices).
  • Ratio (investment ratio, both in US dollar current prices and US dollars 2010 constant prices).US dollar current prices expressed in millions.
  • US dollar 2010 constant prices expressed in millions.
  • Percentage (%) (agriculture share of total).
  • Index (agriculture orientation index).
  • Growth (annual, both in US dollar current prices and US dollars 2010 constant prices).
  • Ratio (investment ratio, both in US dollar current prices and US dollars 2010 constant prices).

Land, Inputs and Sustainability: Land Use

  • 1961 – Present: Agricultural land, Agriculture, Arable land, Country area, Cropland, Inland waters, Land area, Land area equipped for irrigation, Land under permanent crops, Land under permanent meadows and pastures.
  • 1990 – Present: Forest land, Naturally regenerating forest, Other land, Planted Forest.
  • 1990 – 2015: Primary Forest.
  • 2001 – Present: Agriculture area actually Irrigated, Cropland area actually Irrigated, Land under temp. meadows and pastures, Land with temporary fallow, Perm. meadows & pastures – Cultivated, Perm. meadows & pastures – Nat. growing, Permanent meadows and pastures area actually Irrigated.
  • 2004 – Present: Agriculture area Certified Organic, Agriculture area under Organic agriculture, Cropland area Certified Organic, Cropland area under Organic agriculture, Perm. meadows & pastures area Certified Organic, Permanent meadows and pastures area under Organic agriculture.
  • 2006 – Present: Land area actually Irrigated.
  • 2007 – Present: Coastal waters, Inland waters used for aquaculture or holding facilities, Inland waters used for capture fisheries, Land used for aquaculture.
  • 2013 – Present: Coastal waters used for aquaculture or holding facilities, Coastal waters used for capture, Cropland area under Conservation Tillage, Cropland area under conventional Tillage, Cropland area under zero or no Tillage, EEZ waters used for aquaculture or holding facilities, EEZ waters used for capture fisheries, Exclusive Economic Zone (EEZ), Farm buildings and farmyards, Forestry area actually Irrigated, Land under protective cover.
  • Areas values are in 1000 ha (hectares).
  • Carbon stocks values (for forest category only) are in million tonnes. In English speaking countries, a tonne is usually referred to as “metric ton”.

Land, Inputs and Sustainability: Fertilizers by Nutrient

  • Tonnes (t)
  • Nutrient Nitrogen N (Total)
  • Nutrient Phosphate P2O5 (Total)
  • Nutrient Potash K2O (Total)

Land, Inputs and Sustainability: Livestock Manure

  • Amount excreted in manure (N content).
  • Manure left on pasture (N content).
  • Manure left on pasture that volatilises (N content).
  • Manure left on pasture that leaches (N content).
  • Manure treated (N content).
  • Losses from manure treated (N content).
  • Manure applied to soils (N content).
  • Manure applied to soils that volatilises (N content).
  • Manure applied to soils that leaches (N content).
  • Animal stocks (heads and birds)
  • Other elements: kg of Nitrogen (N)

Land, Inputs and Sustainability: Pesticide Use

  • The type of activity/target organism (e.g. fungicides, insecticides, herbicides, plant growth regulators, rodenticides and etc.) and (b) the chemical nature (e.g. organophosphates, pyrethroids). The classification adopted in the Pesticide (use) database is organized as follows:
  • Insecticides: Chlorinated hydrocarbons, Organo–phosphates, Carbamates–insecticides, Pyrethroids, Botanical and biological products and Others not elsewhere classified.
  • Mineral Oils.
  • Herbicides: Phenoxy hormone products, Triazines, Amides, Carbamates–herbicides, Dinitroanilines, Urea derivatives, Sulfonyl urea, Bipiridils, Uracil, Others not elsewhere classified.
  • Fungicides and Bactericides: Inorganic, Dithiocarbamates, Benzimidazoles, Triazoles Diazoles, Diazines Morpholines, Others not elsewhere classified.
  • Seed Treatment – Fungicides: Dithiocarbamates, Benzimidazoles, Triazoles Diazoles, Diazines Morpholines, Botanical products and biological, Others not elsewhere classified.
  • Seed Treatment – Insecticides: Organo-phosphates, Carbamates–insecticides, Pyrethroids, Others not elsewhere classified.
  • Plant Growth Regulators.
  • Rodenticides: Anti–coagulants, Cyanide Generators, Hypercalcaemics, Narcotics, Others not elsewhere classified.
  • Other Pesticides NES: Not elsewhere specified.
  • Indonesia had the highest GDP Value and lowest GDP Share at $107,436,478,883.95 and 13.61%
  • Vietnam had the lowest GDP Value at $28,328,324,531.15
  • Within this cluster, Indonesia is the least dependent on agricultural in regard to their GDP yet still highly dependent when compared to the countries in the same cluster with “High GDP Value, Low GDP Share”. The maximum agriculture GDP Share for “High GDP Value, Low GDP Share” countries was 3.93% (Russia) compared to 22.97% (Pakistan) for “High GDP Value, High GDP Share” countries. Ironically, they both have similar GDP Values of ~$53 billion which shows how dependent one country is compared to the other
  • Tonnes of active ingredients. A tonne is usually referred in English speaking countries as ‘metric ton’.

Population and Employment: Annual Population

  • Population data refers to the World Population Prospects: The 2019 Revision from the UN Population Division.
  • Urban/rural population data refers to the World Urbanization Prospects: The 2018 Revision from the UN Population Division.
  • Total Population – Both sexes.
  • Total Population – Male.
  • Total Population – Female.
  • Rural population.
  • Urban population.
  • Quantity

Population and Employment: Employment Indicators

  • Employment in Agriculture, Forestry and Fishing by Age: The indicator corresponds to the ILOSTAT indicator “Employment by sex, age and economic activity (thousands) – Annual “for agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories:
  • Paid employment (whether at work or with a job but not at work).
  • Self-employment (whether at work or with an enterprise but not at work).
  • Employment in Agriculture, Forestry and Fishing by Status of Employment: The indicator corresponds to the ILOSTAT indicator “Employment by sex, status in employment and economic activity (thousands) — Annual” for agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. The indicator provides information on the status in employment of workers according to the latest version of the International Standard Classification of Status in Employment (ICSE-93). The status in employment refers to the type of explicit or implicit contract of employment the person has with other persons or organizations. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories:
  • Paid employment (whether at work or with a job but not at work).
  • Self-employment (whether at work or with an enterprise but not at work).
  • Employment in Agriculture, Forestry and Fishing by Sub-Sectors (ISIC Divisions- 2 digits): The indicator corresponds to the ILOSTAT indicator “Employment by sex and economic activity – ISIC level 2 (thousands) — Annual” for the divisions of agriculture, forestry and fishing in accordance to the Section A of ISIC classification. The divisions of agriculture are:
  • Crop and animal production, hunting and related service activities.
  • Forestry and logging.
  • Fishing and aquaculture in accordance with the Section A of ISIC classification.

The indicator provides information on the relative importance of crop and animal production, hunting and related service activities, forestry and logging, and fishing and aquaculture in total employment in agriculture, forestry and fishing. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories:

  • Paid employment (whether at work or with a job but not at work).
  • Self-employment (whether at work or with an enterprise but not at work).
  • Mean Weekly Hours Actually Worked per Employed Person in Agriculture, Forestry and Fishing: The indicator corresponds to the ILOSTAT indicator “Mean weekly hours actually worked per employed person by sex and economic activity – Annual” for agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. The indicator provides information on the mean hours worked per week by workers in all types of working time arrangements such as full-time and part-time in the agricultural sector.
  • Mean Weekly Hours Actually Worked per Employee in Agriculture, Forestry and Fishing: The indicator corresponds to the ILOSTAT indicator “Mean weekly hours actually worked per employee by sex and economic activity — Annual” for agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. The indicator provides information on the mean hours worked per week by workers in all types of working time arrangements such as full-time and part-time in the agricultural sector.
  • Employment in Agriculture, Forestry and Fishing – ILO Modelled Estimates: The indicator corresponds to the ILOSTAT indicator “Employment by sex and economic activity — ILO modelled estimates, Nov. 2021 (thousands) — Annual” for agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories:
  • Paid employment (whether at work or with a job but not at work).
  • Self-employment (whether at work or with an enterprise but not at work).
  • Share of Employment in Agriculture, Forestry and Fishing in Total Employment: This indicator gives the share of people employed in agriculture, forestry and fishing among the total employed population. It corresponds to the ILOSTAT indicator called “Employment by sex, age and economic activity (thousands) — Annual” for agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. The indicator provides information on the relative importance of agriculture, forestry and fishing with regard to employment. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories:
  • Paid employment (whether at work or with a job but not at work).
  • Self-employment (whether at work or with an enterprise but not at work).
  • Share of Females in Total Employment in Agriculture, Forestry and Fishing: This indicator gives the share of females employed among the total employed population in the agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories:
  • Paid employment (whether at work or with a job but not at work).
  • Self-employment (whether at work or with an enterprise but not at work).
  • Share of Employment in Agriculture, Forestry and Fishing by Sub-Sectors (ISIC Divisions- 2 digits): This indicator gives the share of employment in division of agriculture, forestry and fishing. It corresponds to the ILOSTAT indicator called “Employment by sex and economic activity – ISIC level 2 (thousands) — Annual” for agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. The divisions of agriculture are:
  • Crop and animal production, hunting and related service activities.
  • Forestry and logging.
  • Fishing and aquaculture in accordance with the Section A of ISIC classification.

The indicator provides information on the relative importance of crop and animal production, hunting and related service activities, forestry and logging, and fishing and aquaculture in total employment in agriculture, forestry and fishing. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories:

  • Paid employment (whether at work or with a job but not at work).
  • Self-employment (whether at work or with an enterprise but not at work).
  • Share of Employees in Agriculture, Forestry and Fishing in Total Employees: This indicator gives the share of employees in agriculture, forestry and fishing among the total employees. It corresponds to the ILOSTAT indicator called “Employees by sex and economic activity” for agriculture includes agriculture, forestry, and fishing in accordance with the Section A of ISIC classification. Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work.
  • Share of Female Employees in Total Employees in Agriculture, Forestry and Fishing: This indicator gives the share of female employees among the total employees in agriculture, forestry and fishing in accordance with the Section A of ISIC classification. Employees are all those workers who hold paid employment jobs, which are those where the incumbents hold employment contracts which give them a basic remuneration not directly dependent upon the revenue of the unit for which they work.
  • Share of Employment in Agriculture, Forestry and Fishing in Total Employment – ILO Modelled Estimates: This indicator gives the share of people employed in agriculture, forestry and fishing among the total employed population. It corresponds to the ILOSTAT indicator “Employment by sex and economic activity — ILO modelled estimates, Nov. 2021 (thousands) – Annual” for agriculture, forestry and fishing sector which is defined in accordance to the Section A of ISIC classification. The indicator provides information on the relative importance of agriculture, forestry and fishing in total employment. Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories:
  • Paid employment (whether at work or with a job but not at work).
  • Self-employment (whether at work or with an enterprise but not at work).
  • Agriculture Value Added per Worker (US$, 2015 prices): Computation based on data from FAOSTAT and ILOSTAT: This indicator provides information on the output of the agricultural sector by worker engaged. It is a measure of agricultural productivity. The data on the value added in agriculture, forestry and fisheries (in US$, 2015 prices) is extracted from FAOSTAT and then divided by the number of people employed in agriculture, forestry and fishing extracted from ILOSTAT for a given year in a given country.
  • Count
  • Share of Total (Percentage)

Production: Crops and Livestock Products

  • Crops Primary: Cereals, Citrus Fruit, Fibre Crops, Fruit, Oil Crops, Oil Crops and Cakes in Oil Equivalent, Pulses, Roots and Tubers, Sugar Crops, Treenuts and Vegetables. Data are expressed in terms of area harvested, production quantity and yield. Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed or silage or used for grazing are therefore excluded.
  • Crops Processed: Beer of barley; Cotton lint; Cottonseed; Margarine, short; Molasses; Oil, coconut (copra); Oil, cottonseed; Oil, groundnut; Oil, linseed; Oil, maize; Oil, olive, virgin; Oil, palm; Oil, palm kernel; Oil, rapeseed; Oil, safflower; Oil, sesame; Oil, soybean; Oil, sunflower; Palm kernels; Sugar Raw Centrifugal; Wine.
  • Live Animals: Animals live n.e.s.; Asses; Beehives; Buffaloes; Camelids, other; Camels; Cattle; Chickens; Ducks; Geese and guinea fowls; Goats; Horses; Mules; Pigeons, other birds; Pigs; Rabbits and hares; Rodents, other; Sheep; Turkeys.
  • Livestock Primary: Beeswax; Eggs (various types); Hides buffalo, fresh; Hides, cattle, fresh; Honey, natural; Meat (ass, bird nes, buffalo, camel, cattle, chicken, duck, game, goat, goose and guinea fowl, horse, mule, Meat nes, meat other camelids, Meat other rodents, pig, rabbit, sheep, turkey); Milk (buffalo, camel, cow, goat, sheep); Offals, nes; Silk-worm cocoons, reelable; Skins (goat, sheep); Snails, not sea; Wool, greasy.
  • Livestock Processed: Butter (of milk from sheep, goat, buffalo, cow); Cheese (of milk from goat, buffalo, sheep, cow milk); Cheese of skimmed cow milk; Cream fresh; Ghee (cow and buffalo milk); Lard; Milk (dry buttermilk, skimmed condensed, skimmed cow, skimmed dried, skimmed evaporated, whole condensed, whole dried, whole evaporated); Silk raw; Tallow; Whey (condensed and dry); Yoghurt.
  • Crops Primary: Areas refer to the area under cultivation. Area under cultivation means the area that corresponds to the total sown area, but after the harvest it excludes ruined areas (e.g. due to natural disasters). If the same land parcel is used twice in the same year, the area of this parcel can be counted twice. Production means the harvested production. Harvested production means production including on-holding losses and wastage, quantities consumed directly on the farm and marketed quantities, indicated in units of basic product weight. Yield means the harvested production per ha for the area under cultivation.
  • Crops Processed: Production quantities in tonnes of processed crops.
  • Live Animals: Stocks in number for Beehives and in heads or 1000 heads for other live animals. Aggregates are the sum of available data. Aggregates include estimated data. For some item aggregates, conversion factors are applied to values when calculating totals.
  • Livestock Primary: Producing Animals/Slaughtered; Yield and Production Quantity. The unit of measure for these variables are as per the following items: “Slaughtering” is measured through activity of slaughterhouses, e.g. production of marketable meat for human consumption. Estimates of ‘other slaughtering’ can be added for a more accurate picture of meat production. “Livestock” is accounted for by categories that capture their rearing, either for fattening then slaughter, or for herd renewal, e.g. for breeding and/or milking. Aggregates are the sum of available data. Aggregates include estimated data. For some item aggregates, conversion factors are applied to values when calculating totals.
  • Livestock Processed: Production quantity of processed commodities from livestock. Aggregates are the sum of available data. Aggregates include estimated data. For some item aggregates, conversion factors are applied to values when calculating totals.
  • Data of Iraq do not include Kurdistan region
  • Since 2007 France data include French Guiana, Martinique, Guadeloupe, Reunion territories but they exclude French Polynesia and New Caledonia territories.
  • Information provided by the Russian Federation includes statistical data for the Autonomous Republic of Crimea and the city of Sevastopol, Ukraine, temporarily occupied by the Russian Federation and is presented without prejudice to relevant UN General Assembly and UN Security Council resolutions, including UN General Assembly resolution 68/262 of 27 March 2014 and UN Security Council resolution 2202 (2015) of 17 February 2015, which reaffirm the territorial integrity of Ukraine. Information provided by Ukraine excludes statistical data concerning the Autonomous Republic of Crimea, the city of Sevastopol and certain areas of the Donetsk and Luhansk regions. The information is presented without prejudice to relevant UN General Assembly and UN Security Council resolutions, including UN General Assembly resolution 68/262 of 27 March 2014 and UN Security Council resolution 2202 (2015) of 17 February 2015, which reaffirm the territorial integrity of Ukraine.
  • Crops Primary: Area Harvested is expressed in hectares (ha), production is expressed in tonnes (t), seed is expressed in tonnes (t), yield is expressed in (hg/ha).
  • Crops Processed: Production is expressed in tonnes (t).
  • Live Animals: Stocks (1000 heads), stocks (No), stocks (heads).
  • Livestock Primary: Laying (1000 heads), milk animals (heads), prod Population (No), prod Population (heads), producing Animals/Slaughtered (1000 heads), producing Animals/Slaughtered (heads), production (1000 heads), production (1000), production (heads), production (t), yield (100 mg/head), yield (No/head), yield (hg/head), yield/Carcass Weight (0.1 g/head), yield/Carcass Weight (hg/head).
  • Livestock Processed: Production is expressed in tonnes (t).

Production: Value of Agricultural Production

  • Crops Primary.
  • Fibre Crop.
  • Primary, Cereals.
  • Coarse Grain.
  • Citrus Fruit.
  • Fruit
  • Jute Jute-like Fibres.
  • Oilcakes Equivalent.
  • Oil crops Primary.
  • Pulses, Roots and Tubers.
  • Treenuts
  • Vegetables
  • Melons

Data are expressed in terms of area harvested, production quantity and yield. The objective is to comprehensively cover production of all primary crops for all countries and regions in the world. Cereals: Area and production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed or silage or used for grazing are therefore excluded. Area data relate to harvested area. Some countries report sown or cultivated area only however, in these countries the sown or cultivated area does not differ significantly in normal years from the area actually harvested, either because practically the whole area sown is harvested or because the area surveys are conducted around the harvest period. Vegetables, total (including melons): Data relate to vegetable crops grown mainly for human consumption. Crops such as cabbages, pumpkins and carrots, when explicitly cultivated for animal feed, are therefore excluded. Statistics on vegetables are not available in many countries, and the coverage of the reported data differs from country to country. In general, it appears that the data refer to crops grown in field and market gardens mainly for sale, thus excluding crops cultivated in kitchen gardens or small family gardens mainly for household consumption. Fruit, total (excluding melons): Data refer to total production of fresh fruit, whether finally used for direct consumption for food or feed, or processed into different products: dry fruit, juice, jam, alcohol, etc. Generally, production data relate to plantation crops or orchard crops grown mainly for sale. Data on production from scattered trees used mainly for home consumption are not usually collected. Production from wild plants, particularly berries, which is of some importance in certain countries, is generally disregarded by national statistical services. Therefore, the data for the various fruits and berries are rather incomplete. Bananas and plantains: Figures on bananas refer, as far as possible, to all edible fruit-bearing species of the genus Musa except Musa paradisiaca, commonly known as plantain. Unfortunately, several countries make no distinction in their statistics between bananas and plantains and publish only overall estimates. When this occurs and there is some indication or assumption that the data reported refer mainly to bananas, the data are included. The production data on bananas and plantains reported by the various countries are also difficult to compare because a number of countries report in terms of bunches, which generally means that the stalk is included in the weight. Dates, plantains and total grapes are included in the “total fruit†aggregated figures, while olives are excluded. Treenuts, aggregated: Production of nuts (including chestnuts) relates to nuts in the shell or in the husk. Statistics are very scanty and generally refer only to crops for sale. In addition to the kind of nuts shown separately, production data include all other treenuts mainly used as dessert or table nuts, such as pecan nuts, pili nuts, sapucaia nuts and macadamia nuts. Nuts mainly used for flavouring beverages are excludedas are masticatory and stimulant nuts and nuts used mainly for the extraction of oil or butter, including areca/betel nuts, cola nuts, illipe nuts, karate nuts, coconuts, tung nuts, oilpalm nuts etc.

  • Production Quantity and Seed: tonnes.
  • Area Harvested: hectares.
  • Yield: tonnes per hectare.

Trade: Crops and Livestock Product

  • Quantity of Food and Agricultural Exports: Export quantity is defined by the IMTS as the physical quantity of domestic origin or manufactured products shipped out of the country. It includes re-exports. According to the FAO methodology, the quantity of food and agricultural exports included in the FAOSTAT database is expressed in terms of weight (tonnes) for all commodities except for live animals which are expressed in units (heads); poultry, rabbits, pigeons and other birds are expressed in thousand units. As a general rule, trade quantity refers to net weight, excluding any sort of container.
  • Value of Agricultural Exports: Value of agricultural exports are expressed in thousand US dollars in the FAOSTAT database. Export values are reported as FOB (free on board— that is, the value of the goods plus the value of the services performed to deliver the goods to the border of the exporting country).
  • Quantity of Food and Agricultural Imports: Import quantity represents the physical quantity of the products imported for domestic consumption or processing shipped into a country. It includes re-imports. According to the FAO methodology, the quantity of food and agricultural imports included in the FAOSTAT database is expressed in terms of weight (tonnes) for all commodities except for live animals which are expressed in units (heads); poultry, rabbits, pigeons and other birds are expressed in thousand units. As a general rule, trade quantity refers to net weight, excluding any sort of container. It includes also food aid quantities, where relevant.
  • Value of Agricultural Imports: Value of agricultural imports are expressed in thousand US dollars in the FAOSTAT database. Import values are reported as CIF (Cost Insurance and Freight), that is, the value of the goods, plus the value of the services performed to deliver goods to the border of the exporting country, plus the value of the services performed to deliver the good from the border of the exporting country to the border of the importing country).
  • Data of Iraq do not include Kurdistan region.
  • Since 2007 France data include French Guiana, Martinique, Guadeloupe, Reunion territories but they exclude French Polynesia and New Caledonia territories.
  • Information provided by the Russian Federation includes statistical data for the Autonomous Republic of Crimea and the city of Sevastopol, Ukraine, temporarily occupied by the Russian Federation and is presented without prejudice to relevant UN General Assembly and UN Security Council resolutions, including UN General Assembly resolution 68/262 of 27 March 2014 and UN Security Council resolution 2202 (2015) of 17 February 2015, which reaffirm the territorial integrity of Ukraine. Information provided by Ukraine excludes statistical data concerning the Autonomous Republic of Crimea, the city of Sevastopol and certain areas of the Donetsk and Luhansk regions. The information is presented without prejudice to relevant UN General Assembly and UN Security Council resolutions, including UN General Assembly resolution 68/262 of 27 March 2014 and UN Security Council resolution 2202 (2015) of 17 February 2015, which reaffirm the territorial integrity of Ukraine.
  • Export Quantity: tonnes
  • Export Value: $USD
  • Import Quantity: tonnes
  • Import Value: $USD

Macro-Economic Indicators: Agriculture Share and Value of GDP

  • GDP measures the total gross value added from all institutional units resident in the economy, at producers’ prices, plus taxes on imports, less subsidies on imports, plus non-deductible VAT (Production approach to GDP). As such, GDP measures the total value created in the production of goods and services by all resident units during the accounting period. The output of most goods or services is usually recorded when their production is completed. However, when it takes a long time to produce a unit of output, it becomes necessary to recognize that output is being produced continuously and to record it as a work-in-progress. For example, the production of certain agricultural goods or large durable goods such as ships or buildings may take months or years to complete. In such cases, it would distort economic reality to treat the output as if it were all produced at the moment of time when the process of production happens to terminate.
  • Gross Fixed Capital Formation (GFCF) is measured by the total value of a producers’ acquisitions, less disposals, of fixed assets during the accounting period plus certain specified expenditure on services that adds to the value of non-produced assets. The boundary line between those products that are retained in the economy and are used for consumption and those products that are used for capital formation is known as the asset boundary. The asset boundary for fixed assets consists of goods and services that are used in production for more than one year. Two exclusions from the asset boundary should be noted. The first is that consumer durables are not treated as fixed assets. The second exclusion is pragmatic rather than conceptual and concerns small tools. Hand tools such as saws, spades, knives, axes, hammers, screwdrivers and spanners or wrenches are examples. If expenditures on such tools take place at a fairly steady rate and if their value is small compared with expenditures on more complex machinery and equipment, it may be appropriate to treat the tools as materials or supplies used for intermediate consumption. In countries in which they account for a significant part of the value of the total stock of an industry’s durable producers’ goods, they may be treated as fixed assets and their acquisition and disposal by producers recorded under gross fixed capital formation. Gross fixed capital formation may take the form of improvements to existing fixed assets, such as buildings and structures, that increase their productive capacity, extend their service lives, or both. A different treatment is applied to improvements to land in its natural state. In this case the improvements are treated as the creation of a new fixed asset and are not regarded as giving rise to an increase in the value of the natural resource. If land, once improved, is further improved, then the normal treatment of improvements to existing fixed assets applies.
  • The Investment Ratio (IR) is obtained as the GFCF share to GDP.
  • Gross Output (GO), at the most granular level, gross output consists of those goods and services that are produced within an establishment that become available for use outside that establishment, plus any goods and services produced for own final use. Industry gross output is the market value of the goods and services produced by an industry.
  • Value Added (VA) represents the contribution of labor and capital to the production process. Gross output relates directly to the concept of value added as the latter is obtained by subtracting the value of intermediate consumption evaluated at purchasers’ prices from the value of output at basic prices. Although the outputs and inputs are valued using different sets of prices, for brevity the value added is described by the prices used to value the outputs. From the point of view of the producer, purchasers’ prices for inputs and basic prices for outputs represent the prices actually paid and received. Their use leads to a measure of gross value added that is particularly relevant for the producer. Net value added is defined as the value of output less the values of both intermediate consumption and consumption of fixed capital.
  • Gross National Income (GNI) is a measure of total incomes receivable in the economy. It is equal to GDP less primary incomes payable to non-resident units plus primary incomes receivable from non-resident units. In other words, GNI is equal to GDP less taxes (less subsidies) on production and imports, compensation of employees and property income payable to the rest of the world plus the corresponding items receivable from the rest of the world.
  • USD at current prices and at constant 2015 prices.
  • Percentage for growth rates and share variables.

B. Github

Country Codes

  • Anuar Ustayev (aka Anu)
  • Meiran Zhiyenbayev
  • Muhammad Ismail Shahzad
  • Rufus Pollock
  • Søren Jones
  • Yann-Aël Le Borgne
  • Irakli Mchedlishvili

C. Citation

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