Single Point Corn Yield Data - Weather, Soil, Cultivation Area, and Yield for Precision Agriculture

Published: 20 June 2024| Version 1 | DOI: 10.17632/dkv6b3xj99.1
Chollette Olisah


This data comprises processed weather, soil, yield, and cultivation area for corn yield prediction in Sub-Sahara Africa, with emphasis on Nigeria. The data was collected to design a corn yield prediction model to help smallholder farmers make smart farming decisions. However, the data can serve several other purposes through analysis and interpretation. The reference study region in Africa is Nigeria. The focuses on corn crop because there are over 211.4 million people, of which a large percentage of the population are smallholder farmers. Nigeria [9.0820° N, 8.6753° E] is within an arable land area of 34 million hectares located on the west coast of Africa. The region comprises of 36 states with the most and least number of districts being 214 and 10, respectively. For each state, the environment data are collected as follows. Grid map climate data – This data spans spatial resolutions between ~1 km2 to ~340 km2 from the high spatial resolution WorldClim global climate database22. Each grid point on the map is monthly data from January to December between 1970 and 2000 years and records 8 climate variables. The variables are average temperature C0, minimum temperature C0, maximum temperature C0, precipitation (mm), solar radiation (kJ m^(-2) day(-1), wind speed (m s(-1)), and water vapor (kPa) taken at 30 seconds (s), 2.5 minutes m, 5 m, and 10 m. Grid map soil data – This data is obtained from 250 minutes of spatial resolution AfSIS soil data23 from year 1960 to 2012. The variables are wet soil bulk density, dry bulk density (kg dm-3), clay percentage of plant available water content, hydraulic conductivity, the upper limit of plant available water content, the lower limit of, organic matter percentage, pH, sand percentage (g 100 g-1), silt percentage (g 100 g-1) and, clay percentage (g 100 g-1), and saturated volumetric water content variables measured at depths 0–5, 5–10, 10–15, 15–30, 30–45, 45–60, 60–80, 80–100, and 100–120 measured in centimeters (cm). Corn yield data – This data is available on Kneoma Corporation website24. It ranged from years 1995 to 2006 and consisted of a corn yield of 1000 metric tonnes and a cultivation area of 1000 hectares. Geolocation coordinates (latitude and longitude) – The geolocation of each of the 36 states with their districts is sampled from Google Maps. The output feds into the Esri-ArcGIS 2.5, a professional geographical software, for extracting the point-cloud values of each environmental variable (weather and soil) at specific geolocation of the 36 states of Nigeria. Other Descriptions: Data type - Continous and Categorical Dataset Characteristics - Tabular Associated Tasks - Regression Feature Type - Real Number of Instances - 1828 Number of Features: 12


Steps to reproduce

Code Availability The source code is available on GitHub in the following link: Please cite the paper as follows: @misc{title={Corn Yield Prediction Model with Deep Neural Networks for Smallholder Farmer Decision Support System}, author={Chollette Olisah, Lyndon Smith, Lawrence Morolake, Osita Ojukwu}, year={2024}, eprint={arXiv:2401.03768}, archivePrefix={arXiv}, primaryClass={cs.LG} } }


University of the West of England Bristol


Precision Agriculture, Decision Support System in Agriculture, Data Collection in Agriculture, Farming System Innovation, Deep Learning, Applied Machine Learning, Climate-Smart Agriculture


Global Challenges Research Fund