Project – Adoption of Precision Agriculture Technologies and Farm Productivity among Commercial Vegetable Farmers: A Study of Farmers in Ogun State

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Agriculture remains an important component of economic development, food security and rural livelihoods in Nigeria, but the productivity of many farming systems continues to be affected by challenges such as climate variability, inefficient input use, limited access to modern technologies and inadequate technical support. Vegetable production is particularly important because vegetables provide essential nutrients and constitute an important source of income for commercial farmers supplying urban and peri-urban markets. However, vegetable farmers often operate under conditions in which land, water, fertilizer, pesticides, labour and other production resources must be carefully managed to maintain profitable yields. The increasing application of digital and data-driven technologies provides an opportunity to improve this situation. The Food and Agriculture Organization (FAO, 2021) explains that precision agriculture is a data-driven approach to farm management that can improve productivity and yields while reducing unnecessary use of water, fertilizers and pesticides. Consequently, the application of precision agriculture technologies has become increasingly relevant to efforts to improve the efficiency and sustainability of agricultural production.

Precision agriculture refers to the use of technologies, information and data to manage agricultural activities according to the specific conditions of a farm, field or production area rather than applying uniform decisions across the entire farm. Technologies associated with precision agriculture include global positioning systems (GPS), geographic information systems (GIS), remote sensing, drones or unmanned aerial vehicles, soil and crop sensors, satellite imagery, mobile applications, automated equipment, variable-rate technologies and digital farm-management platforms. These technologies can assist farmers in identifying variations in soil conditions, crop health, moisture levels, pest occurrence and nutrient requirements, thereby allowing farm decisions to become more targeted. FAO (2021) notes that advances in mobile phones, remote sensing, UAVs, the Internet of Things, artificial intelligence and cloud computing are making precision-agriculture applications increasingly accessible to smallholder farmers in developing countries, although barriers such as digital literacy, infrastructure and technical skills can restrict adoption. Thus, precision agriculture is not simply the introduction of sophisticated machinery but a broader approach to using information and technology to make farm management more precise.

The potential importance of precision agriculture becomes clearer when considering its relationship with farm productivity. Agricultural productivity depends not only on the quantity of land cultivated but also on how effectively farmers combine land, labour, water, seed, fertilizer, pesticides, machinery, information and other inputs to generate output. Precision technologies can support this process by improving the timing and accuracy of farm operations and by helping farmers allocate inputs according to observed production conditions. Evidence from rural Nigeria indicates that agricultural technology adoption is associated with important productivity considerations. A nationally representative plot-level study by Ochieng, Kirimi and Mathenge (2023) examined the adoption of multiple agricultural technologies and sustainable agricultural practices in Nigeria and found that adoption and intensity of use were influenced by factors including climate conditions, extension access, education and household characteristics. The study also found that the productivity implications of technology adoption varied across different practices, demonstrating the importance of examining both adoption and actual farm-level outcomes rather than assuming that every technology automatically produces the same productivity effect.

The relevance of digital agriculture is also increasing as farmers gain access to mobile phones, smartphones, internet services and agricultural applications. Digital tools can provide farmers with information concerning weather, pest management, fertilizer application, crop management, markets and other production decisions. In Ogun State, Abioye et al. (2024) investigated farmers’ willingness to adopt digital application tools, focusing on the IITA Herbicide Calculator and Akilimo mobile applications. Using data from 572 smallholder farmers, the researchers found that education, training, internet access, smartphone ownership, awareness and the cost of digital applications were among the factors associated with willingness to adopt digital agricultural tools. The study is particularly relevant to the present research because it demonstrates that Ogun State already provides an empirical setting in which farmers’ engagement with digital agricultural technologies can be examined. However, the study focused on willingness to adopt selected digital applications and cassava farmers rather than the actual adoption of a broader range of precision agriculture technologies among commercial vegetable farmers and its relationship with farm productivity.

The adoption of precision agriculture technologies among farmers is not determined solely by the availability of technology. Farmers’ knowledge, education, access to extension services, financial resources, perceived usefulness, technological skills, infrastructure and expected economic benefits can influence whether a technology is adopted and used consistently. Oyekunle et al. (2025), in a study of factors influencing adoption of precision agriculture technologies for food production at the International Institute of Tropical Agriculture in Nigeria, found that farmers could be aware of precision technologies and willing to use them while still facing a gap between awareness and actual implementation. The study identified government support, access to information, education, affordability, expected income gains and land-tenure security as relevant considerations in technology adoption. Similarly, Ojo et al. (2024) found that education, farming experience, income, extension exposure, credit access, risk orientation and cooperative membership were among factors influencing the adoption and intensity of climate-smart agricultural practices among farmers in Nigeria. These findings suggest that technology adoption must be considered in relation to the institutional, economic and human capacities of farmers.

Vegetable farming presents particular opportunities for precision agriculture because vegetable crops often require careful management of water, nutrients, pests, diseases, soil conditions and harvesting periods. Small differences in moisture, nutrient availability or pest pressure can influence crop quality and marketable yield, making timely and site-specific management potentially valuable to commercial vegetable farmers. Evidence from Ogun State indicates that vegetable farmers already experience productivity and efficiency challenges. Oladeji, Adekunle, Tolorunju and Osinowo (2019), in a study of organic and inorganic leafy vegetable production in Ogun State, reported a mean technical efficiency of 0.679 among the sampled farmers, indicating room for improvement in the efficiency with which production resources were converted into output. More recent research in Nigeria also demonstrates that technology-oriented production systems can improve resource efficiency and productivity. Ayinde, Nicholson and Ahmed (2025), for example, found that screenhouse vegetable production in Nigeria produced substantially more saleable output and used considerably less water per kilogram of produce than rainfed systems in their study area. These findings support the relevance of examining whether technology-based production approaches can contribute to improved productivity among commercial vegetable farmers.

The need to examine precision agriculture adoption among commercial vegetable farmers in Ogun State is further strengthened by the growing interest in digital transformation of agriculture and the limited occupation-specific evidence concerning vegetable farmers. Ogun State has a substantial agricultural sector and has been used as a setting for research on digital agricultural applications, sustainable agricultural practices and vegetable production. Recent evidence shows that farmers in Ogun State are willing to engage with digital agricultural technologies, but awareness, training, infrastructure, cost and access remain important considerations. At the national level, Yusuf and Akinola (2025) examined precision agriculture among 250 smallholder farmers in Oyo State and used a precision-agriculture adoption index to investigate its relationship with economic sustainability, measured by net farm income per hectare. The study reflects the growing Nigerian research interest in moving beyond general awareness of agricultural technology to examining the economic consequences of actual adoption. Nevertheless, there remains a need for evidence specifically concerning commercial vegetable farmers in Ogun State and the extent to which technologies such as GPS/GIS applications, digital advisory tools, remote sensing, sensors, drones, precision input-management technologies and related digital tools are associated with farm productivity.

1.2 Statement of the Problem

The productivity of commercial vegetable farming in Nigeria is affected by several interacting challenges, including inefficient input use, climate variability, limited access to agricultural technologies, inadequate extension services and financial constraints. Vegetable farmers must make decisions about irrigation, fertilizer, pesticides, planting periods, pest control and harvesting while operating within increasingly uncertain production environments. Although precision agriculture offers technologies capable of improving the accuracy and timing of such decisions, adoption remains uneven among farmers. FAO (2021) identifies limited digital infrastructure, inadequate digital skills and other implementation barriers as important constraints to the wider use of precision agriculture among smallholder farmers. Nigerian evidence similarly indicates that farmers’ adoption of agricultural technologies is affected by extension access, education, household resources and environmental conditions. The problem, therefore, is that the potential benefits of precision agriculture may not be fully realized when commercial vegetable farmers lack access to, knowledge of or capacity to effectively use these technologies.

A second problem concerns the actual level of adoption of precision agriculture technologies among commercial vegetable farmers. The availability of agricultural applications and digital technologies does not necessarily mean that farmers use them regularly in their production activities. Evidence from Ogun State shows that farmers’ willingness to adopt digital agricultural applications is influenced by education, training, internet access, smartphone ownership, awareness and cost. Abioye et al. (2024) also observed that awareness of specific digital tools remained relatively low despite farmers’ willingness to adopt them. This creates an important distinction between awareness, willingness and actual adoption. A farmer may have heard about GPS-based farm mapping, mobile crop-management applications, digital weather information, remote sensing or precision input tools without incorporating these technologies into routine farm decisions. The extent to which commercial vegetable farmers in Ogun State have moved from awareness to actual and consistent adoption therefore requires empirical investigation.

A third problem relates to whether adoption of precision agriculture technologies translates into measurable improvements in farm productivity. Precision agriculture is often promoted because it can help farmers optimize inputs, improve production decisions and potentially increase yields and profitability. However, technology adoption does not automatically produce identical outcomes across different farming systems. The Nigerian plot-level evidence of Ochieng, Kirimi and Mathenge (2023) demonstrates that different agricultural technologies can have different adoption patterns and productivity implications, while Yusuf and Akinola (2025) specifically examined precision agriculture adoption in relation to economic sustainability among smallholder farmers. In vegetable production, productivity may be reflected in crop yield per hectare, quantity of marketable produce, input efficiency, production cost, farm income and the ability to maintain output under variable climatic conditions. The problem is therefore that insufficient empirical evidence is available concerning the extent to which precision agriculture adoption is associated with these dimensions of productivity among commercial vegetable farmers in Ogun State.

Finally, there is a specific geographical and occupational research gap concerning commercial vegetable farmers in Ogun State. Existing research in Ogun State has examined farmers’ willingness to adopt digital applications, sustainable agricultural practices and the technical efficiency of leafy vegetable production, but these areas have generally been investigated separately. Abioye et al. (2024) focused on digital application adoption among smallholder farmers, particularly cassava farmers; Oladeji et al. (2019) examined the technical efficiency of organic and inorganic leafy vegetable production; and Ojo et al. (2024) examined climate-smart agricultural practices among crop farmers. These studies provide useful evidence but do not directly establish the relationship between the adoption of precision agriculture technologies and farm productivity among commercial vegetable farmers. Consequently, there is a need for a focused study that identifies the precision technologies adopted by commercial vegetable farmers, examines the extent of their use and determines how such adoption relates to farm productivity in Ogun State.

1.3 Purpose of the Study

The general purpose of this study is to examine the adoption of precision agriculture technologies and farm productivity among commercial vegetable farmers in Ogun State.

Specifically, the study seeks to:

1. examine the relationship between digital farm-management and advisory technologies and farm productivity among commercial vegetable farmers in Ogun State;
2. determine the relationship between precision soil, water and input-management technologies and farm productivity among commercial vegetable farmers in Ogun State;
3. examine the relationship between remote sensing, GPS/GIS and crop-monitoring technologies and farm productivity among commercial vegetable farmers in Ogun State; and
4. determine the relationship between farmers’ knowledge, training and technical support for precision agriculture and farm productivity among commercial vegetable farmers in Ogun State.

1.4 Research Questions

The following research questions will guide the study:

1. What is the relationship between digital farm-management and advisory technologies and farm productivity among commercial vegetable farmers in Ogun State?
2. What is the relationship between precision soil, water and input-management technologies and farm productivity among commercial vegetable farmers in Ogun State?
3. What is the relationship between remote sensing, GPS/GIS and crop-monitoring technologies and farm productivity among commercial vegetable farmers in Ogun State?
4. What is the relationship between farmers’ knowledge, training and technical support for precision agriculture and farm productivity among commercial vegetable farmers in Ogun State?

1.5 Research Hypothesis

The following null hypothesis will be tested at the 0.05 level of significance:

H₀: There is no significant relationship between the adoption of precision agriculture technologies and farm productivity among commercial vegetable farmers in Ogun State.

1.6 Significance of the Study

The study will be significant to commercial vegetable farmers because it will provide information on the potential relevance of precision agriculture technologies to farm productivity. The findings may help farmers understand how digital advisory applications, precision input-management tools, crop-monitoring technologies and related innovations can be incorporated into vegetable production decisions.

The study will be useful to agricultural extension officers because it may identify areas where farmers require additional knowledge, training and technical assistance in the use of precision agriculture technologies. This is important because evidence from Ogun State indicates that training, education, awareness and extension-related support influence farmers’ willingness to adopt digital agricultural applications.

The study will also benefit government agricultural agencies and policymakers. The findings may provide evidence that can guide policies and programmes designed to increase access to agricultural technologies, improve digital infrastructure, strengthen extension services and reduce barriers to technology adoption among commercial farmers.

The study will be relevant to agricultural technology developers and agribusiness companies because it may identify the types of precision technologies that are most relevant to commercial vegetable farmers and the constraints that affect their adoption. Information on farmers’ technological needs could support the development of more affordable, accessible and user-friendly agricultural applications and services.

The study will also be useful to financial institutions, agricultural development programmes and farmer cooperatives. Evidence concerning the relationship between precision technology adoption and productivity may assist these stakeholders in designing financing, training and technology-support programmes targeted at commercial vegetable producers.

Finally, the study will contribute to academic and research knowledge by providing evidence on precision agriculture adoption within the specific context of commercial vegetable farming in Ogun State. It may serve as a reference for future researchers examining agricultural technology adoption, farm productivity, digital agriculture, precision farming and sustainable vegetable production in Nigeria.

1.7 Scope of the Study

The study focuses on the adoption of precision agriculture technologies and farm productivity among commercial vegetable farmers in Ogun State, Nigeria.

The independent variable, adoption of precision agriculture technologies, will be examined through four dimensions:

• digital farm-management and advisory technologies;
• precision soil, water and input-management technologies;
• remote sensing, GPS/GIS and crop-monitoring technologies; and
• farmers’ knowledge, training and technical support.

The dependent variable is farm productivity, which will be examined using indicators such as crop yield, quantity of marketable produce, input-use efficiency, production cost, farm income and output per unit of cultivated land.

The study will be geographically limited to selected commercial vegetable farmers in Ogun State. It will focus on farmers who produce vegetables for commercial purposes rather than household subsistence production.

1.8 Operational Definition of Terms

Adoption: The decision and actual process by which a farmer accepts, acquires and uses a precision agriculture technology in regular farming activities.

Precision Agriculture: A data-driven approach to farm management that uses technology and information to make farm decisions more accurate, site-specific and efficient.

Precision Agriculture Technologies: Digital, electronic, geospatial and sensor-based technologies used to improve agricultural decision-making and the efficient management of farm resources.

Digital Farm-Management Technology: Mobile applications, digital platforms and electronic tools used to support farm planning, record keeping, weather information, crop management, input recommendations and other farm decisions.

Precision Soil Management: The use of soil information, sensors, mapping or other technologies to identify differences in soil conditions and guide site-specific management of soil nutrients and other soil-related inputs.

Precision Water Management: The use of technology and data to determine the timing, quantity and location of irrigation or water application according to crop and soil requirements.

Precision Input Management: The targeted application of inputs such as fertilizer, pesticides, seeds and water according to the specific needs of crops or portions of a farm.

GPS (Global Positioning System): A satellite-based positioning system that can be used by farmers to determine precise geographical locations and support farm mapping and field management.

GIS (Geographic Information System): A computer-based system for collecting, storing, analysing and displaying geographically referenced agricultural information.

Remote Sensing: The collection of information about crops, soil or farm conditions from a distance through satellites, aircraft, drones or other sensing technologies.

Crop Monitoring: The systematic observation of crop growth, health, moisture, pest conditions or other characteristics using direct observation or technological tools.

Farm Productivity: The efficiency with which farm resources such as land, labour, fertilizer, water, pesticides and capital are transformed into agricultural output. In this study, it will be reflected through yield, marketable output, input efficiency, production cost and farm income.

Commercial Vegetable Farmer: A farmer who cultivates vegetables primarily for sale and income generation rather than solely for household consumption.

Digital Agriculture: The application of digital technologies, data and information systems to agricultural production, marketing, extension and farm-management activities.

Farm Productivity Improvement: An increase in agricultural output or efficiency resulting from better use of production resources, technology, knowledge or management practices.

Project – Adoption of Precision Agriculture Technologies and Farm Productivity among Commercial Vegetable Farmers: A Study of Farmers in Ogun State