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
Feature Engineering
Figure 1: AWMA
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
Model Hyperparameter Tuning
Figure 2: Elbow Method
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
Analysis
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 $53,677,018,322.42, respectively), yet their agriculture GDP shares vary significantly (22.97% and 3.93%, respectively). Most countries in Clusters 1 and 4 mainly fall into the low agriculture GDP value zone, but they display variations when considering agriculture GDP share. All countries in Cluster 2 are situated in the high agriculture GDP value and low agriculture GDP share zone, indicating that these are developed countries. Cluster 3 consists of countries with low agriculture GDP values and low agriculture GDP shares, signifying that these countries make minimal contributions to their GDP through agriculture and are not heavily dependent on it. Overall, the addition of GDP data provided valuable insights into the clusters, allowing us to identify variations in agriculture GDP value, share, and dependence on agriculture across different countries and clusters.
After comprehending the segmented distribution of GDP Ranking and clusters, the subsequent step involved comparing them. The dashboard visualization titled “Average GDP Value in Agriculture ($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.
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
Investment: Country Investment Statistics Profile
Land, Inputs and Sustainability: Land Use
Land, Inputs and Sustainability: Fertilizers by Nutrient
Land, Inputs and Sustainability: Livestock Manure
Land, Inputs and Sustainability: Pesticide Use
Population and Employment: Annual Population
Population and Employment: Employment Indicators
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:
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:
Production: Crops and Livestock Products
Production: Value of Agricultural Production
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.
Trade: Crops and Livestock Product
Macro-Economic Indicators: Agriculture Share and Value of GDP
B. Github
Country Codes
C. Citation
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