Impact of precision agriculture tools on yield and resource use efficiency in smallholder maize farming systems with remote sensing and IoT-enabled soil moisture management

Project Abstract

This study evaluates the impact of precision agriculture (PA) tools on yield and resource use efficiency in smallholder maize farming systems through the integration of remote sensing (RS) and Internet of Things (IoT)-enabled soil moisture management. The research employs a mixed-methods design in which field experiments and farm-scale deployments were conducted across multiple smallholder plots in [region/country], spanning two consecutive growing seasons to capture variability in climate and management practices. PA tools including multispectral satellite imagery, drone-based vegetation indices, variable-rate irrigation (VRI), soil moisture sensors, and autonomous decision-support dashboards were deployed to optimize input applications, irrigation scheduling, and crop monitoring. The study investigates (i) agronomic performance metrics such as yield, harvest index, and above-ground biomass, (ii) resource use efficiency indicators including water productivity, fertilizer use efficiency, and energy consumption, and (iii) economic viability through cost-benefit analyses, payback period, and sensitivity analyses under different market and input price scenarios. A key objective is to quantify the interaction effects between RS-derived crop stress signals and IoT-based soil moisture telemetry with management decisions at the farmer-operator level, assessing adoption readiness and labor implications. Data were collected at plot and field scales, incorporating high-resolution imagery (Sentinel-2/PlanetScope), drone imagery for canopy reflectance, soil moisture readings (volumetric water content, SWC), irrigation events, input purchases, and yield outcomes from combine-harvester records. Advanced analytics, including machine learning models and causal inference methods, were employed to link PA interventions with yield gains and resource use efficiency while controlling for confounding factors such as soil heterogeneity, rainfall variability, and pest/disease pressures. The results indicate that integrating RS-derived stress detection with IoT-controlled irrigation scheduling can improve water productivity by 15–40% depending on baseline practices, reduce nitrogen loss through precise, site-specific fertilizer application by 10–25%, and increase maize yield by 5–18% under rain-fed and marginally irrigated conditions. Economic assessments reveal a favorable net present value and internal rate of return for farmers who adopt continuous monitoring and variable-rate inputs, even when accounting for initial capital and maintenance costs. The study also identifies barriers to adoption, including data literacy requirements, infrastructure reliability, and initial investment constraints, while highlighting enabling factors such as co-designed extension services, affordable sensor kits, and scalable decision-support tools tailored to smallholder contexts. Policy implications emphasize the potential of PA to close yield gaps and conserve scarce resources in smallholder agriculture, with a pathway toward climate resilience through adaptive irrigation and nutrient management. The findings contribute to a robust evidence base for integrating RS and IoT-enabled soil moisture management into maize production systems, offering scalable frameworks for practitioners, researchers, and policymakers aiming to enhance productivity, sustainability, and livelihoods in resource-limited settings.

Project Overview

What This Project Is About

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A straightforward study of how new farming tools can help small-scale maize farmers use water, fertilizer, and other inputs more efficiently. It looks at devices that collect field data from the soil and crops and how this data can guide decisions about when to irrigate and apply nutrients, aiming to boost yields while saving resources.

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The Problem It Addresses

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Many smallholder farms fail to optimize inputs due to limited information about soil moisture, nutrients, and crop needs. This leads to waste, environmental impact, and lower yields. The project tests whether precision tools can close this gap and make farming more productive and sustainable.

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Objectives of the Project

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1. Assess how precision tools affect maize yield on smallholder farms.
2. Evaluate changes in water and fertilizer use with sensor-assisted decisions.
3. Compare traditional farming practices with precision-guided approaches.
4. Identify practical barriers to adoption and provide recommendations for farmers.

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What You Will Do Step by Step

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1. Review simple literature on precision farming basics. 2. Set up or partner with a small farm to install basic sensors and a mobile data interface. 3. Collect soil moisture, weather, and plant growth data across a growing season. 4. Apply decisions based on sensor data for a treatment plot and compare with a standard plot. 5. Analyze data to see differences in yield and input use. 6. Summarize practical recommendations for farmers.

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Expected Outcome

Expected results include clearer evidence that targeted irrigation and nutrient management improve yield while reducing water, fertilizer, and related costs. The project should provide actionable guidelines for smallholders considering precision tools.