Beyond the Fields: A Data-Driven Look at Kenya’s Agricultural Productivity

1. Overview

Kenya's economy is based primarily on agriculture, which makes a substantial contribution to employment, food security, and GDP. Farming is the main source of income for more than 70% of rural households. However, due to variations in crop selection, agricultural methods, input usage, and susceptibility to weather variations, productivity and profitability fluctuate greatly.

A wealth of data on farmers' activities throughout counties can be found in the Kenya Crops Dataset. The data provide an insight on crop types, planted areas, yields, expenses, income, and management techniques (pest control, irrigation, and fertilizer).

This article analyses the data set to identify trends, obstacles, and possibilities that can guide practice and policy in Kenya's agriculture industry.

2. Data Overview

The dataset consists of 500 records drawn from various counties in Kenya, each providing detailed information on:

• Crops: Include crops such as: - Potatoes, maize, tomatoes, sorghum, coffee, beans, and others.
• Land use: Planted area in acres.
• Productivity: Yield in kilograms.
• Economics: Market prices, revenues, production costs, and profits.
• Farming practices: Fertilizer type, irrigation use, pest control methods.
• Environmental impact: Weather conditions and soil type.

The data's comprehensiveness makes it feasible to evaluate farming's technical and financial facets, as well as environmental influences.

3. Key Findings

3.1 Crop Distribution

The dataset reveals a wide variety of crops grown across counties, with the most popular ones being coffee, potatoes and tea (Fig1).

3.2 Profitability and Yields

Profitability is highly variable among the various crop types. Some farmers record substantial profits, especially those producing high-value crops like rice and sorghum (Fig2).

The yields were highest for rice but sorghum registered moderate yield though revenue and profit were high(Fig2 and Fig3).

3.3 Farming Practices

According to the data set, majority of the farmers did not use any fertilizer. For those who used fertilizer, the preferences favored were DAP, manure and CAN (Fig4).

Irrigation adoption is uneven, with many farmers relying solely on rainfall, exposing yields to droughts and irregular rainfall. Pest control is inconsistently applied, with some farmers report no control methods, exposing crops to risks of pest infection (Fig 5).

Missing Data in the Dataset and Effect on Analysis

Missing Data

One of the issues with the Kenya Crops Dataset is the existence of partial or missing data, which is frequently indicated by "Not Provided" items. Key variables that have missing values include:

• Yield (Kg): In certain instances, farmers failed to document or supply real yields.
• Profit (KES): Economic analysis is less accurate when profit numbers are missing.
• Fertilizer Used: "Not Provided" restricts information about input usage trends.
• Pest Control Method: Without this information, crop protection measures cannot accurately be evaluated.

Effect on Analysis

• Decreased Average and Total Accuracy: The accuracy of computed averages, totals, and comparisons between crops and counties is reduced when yield and profit figures are missing. For instance, if high-profit records are absent, the average profit per crop can be understated.
• Prejudiced Views: Results may disproportionately represent farmers with better-organized records if missing data is not random (smallholder farmers are less likely to record inputs, for example), which would tilt analysis in favor of bigger or better-resourced farms.
• Challenges in Trend Analysis: Trends in modern versus ancient farming methods are difficult to determine due to incomplete information on pest management or fertilizer practices. As a result, it becomes harder to connect input utilization to productivity results.
• Problems with Policy Suggestions: To make focused judgments, policymakers depend on comprehensive datasets. Missing values could conceal crops that require assistance or underperforming areas.

Dashboards in Visualizing Findings

Dashboards convert complicated statistics into understandable, useful insights that enhance in-depth reporting. Intuitive analysis on this dataset is contained in my Dashboard titled: Kenya Crops Analysis Dashboard (Fig 6) Kenya Crops Analysis Dashboard

While reports such as this provide detailed analysis, dashboards make it easier to visualize findings interactively and monitor trends in real time. A dashboard built on this dataset included:

• Crop Distribution: A detailed bar chart containing the distribution of planting area, yield, revenue and profit across counties.
• Impact of weather on revenue and profitability: column chart comparing revenue and profits per weather impact.
• Visual map: giving the position of the counties and their performance.
• Key Metrics (KPIs): Cards displaying total planted area, total yield, total production cost, total revenue and total profit.

The dashboard would enable farmers, researchers, and politicians to:

• Rapidly determine which crops and areas are performing well and poorly.
• Evaluate how well pest management, fertilization, and irrigation are working.
• Monitor how the weather affects crops and earnings.
• Make decisions based on evidence to promote the expansion of agriculture.

Recommendations

Based on the analysis, the following measures could help enhance agricultural outcomes in Kenya:

• Increase irrigation coverage to lessen susceptibility to fluctuations in rainfall and enhance smallholder irrigation projects.
• Encourage sustainable farming methods that preserve soil health, support organic substitutes, soil conservation, and balanced fertilizer use.
• Enhance pest control to reduce agricultural losses, and teach farmers integrated pest management techniques.
• Digitize agricultural records to minimize missing data and enhance decision-making. This will also enhance data quality and monitoring using digital platforms.
• Targeted assistance for low-yield counties by offering training, extension services, and resources to counties that continuously perform poorly.

References and Additional Readings

• Food and Agriculture Organization of the United Nations (FAO). (2021). The State of Food and Agriculture. Rome: FAO.

• Kenya Ministry of Agriculture. (2020). Agricultural Sector Transformation and Growth Strategy (ASTGS) 2019–2029. Nairobi: Government of Kenya.

• World Bank. (2019). Agricultural Productivity in Kenya: Trends and Determinants. Washington, DC: World Bank.

• International Fund for Agricultural Development (IFAD). (2022). Climate-Resilient Agriculture in East Africa. Rome: IFAD.