







































 

 

 
177 

© 2025 Conscientia Beam. All Rights Reserved. 

Design and optimization of a single-row potato harvester for smallholder farmers   

 

 

 Martin Mang’eni 
Nanje1+ 

 Ayub Njoroge 
Gitau2 

 Duncan Onyango 
Mbuge3 

1,2,3Department of Environmental and Biosystems Engineering, University of 
Nairobi, Nairobi, Kenya. 
1Email: martinnanje30@gmail.com  
2Email: ayub.gitau@uonbi.ac.ke  
3Email: duncan.mbuge@uonbi.ac.ke  

 

 
(+ Corresponding author) 

 ABSTRACT 
 
Article History 
Received: 5 August 2025 
Revised: 14 November 2025 
Accepted: 8 December 2025 
Published: 19 December 2025 
 

Keywords 
Design Optimization 
Field performance 
Mechanization 
Potato harvesting 
Smallholder farms 
Soil variability 
Tuber damage rate. 

 
Mechanisation remains vital for increasing productivity and reducing labour demands in 
small-scale agriculture. In Kenya, many smallholder potato farmers still depend on 
inefficient manual harvesting methods, which raise labour costs, post-harvest losses, and 
lower farm profitability. This study details the design, construction, and optimisation of 
an affordable single-row potato harvester tailored for the agro-ecological and socio-
economic conditions of smallholder farms. The prototype combines a digging blade, soil-
sifting unit, and tuber collection system powered by a compact petrol engine to ensure 
affordability, simplicity, and ease of adaptation. Field trials were carried out in 
representative potato-growing regions with soils ranging from sandy to loamy, and from 
wet to dry. Performance was evaluated based on harvesting efficiency, tuber damage rate, 
labour input, and soil contamination. The harvester achieved a 70-75% reduction in 
labour time (4.5–5 hours/ha vs. 15–20 hours/ha manual) and demonstrated tuber 
damage rates below 1% in favorable conditions, substantially mitigating the typical >20% 
post-harvest losses associated with manual methods. These findings demonstrate that 
locally designed mechanisation can significantly improve harvesting efficiency and 
profitability for smallholder farmers. The study recommends increasing production, 
providing farmer training, and further optimising the harvester to enhance performance 
in challenging soil conditions. Overall, the work supports sustainable mechanisation 
practices that strengthen rural livelihoods and food security in regions where potatoes 
are grown. 
 

Contribution/Originality: This study contributes to the existing literature by uniting design modeling, 

structural optimization, and empirically validated performance analysis into a coherent engineering framework for 

smallholder potato harvesters. It advances prior work by simultaneously addressing tuber integrity, soil variability, 

and operational efficiency dimensions that have not been comprehensively examined together in previous research. 

 

1. INTRODUCTION 

Potato farming is a cornerstone of Kenya’s agricultural economy, ranking as the second most important staple 

food crop after maize. It plays a critical role in food security, rural employment, and income generation, particularly 

among smallholder farmers, who constitute over 70% of the country’s potato producers [1]. The crop is 

predominantly cultivated in high-altitude regions such as the Central Rift Valley and the Eastern Highlands, areas 

that offer favorable agro-climatic conditions for potato production. Despite its importance, the sector faces persistent 

Current Research in Agricultural Sciences 
2025 Vol. 12, No. 2, pp. 177-192 
ISSN(e): 2312-6418 
ISSN(p): 2313-3716 
DOI: 10.18488/ 
© 2025 Conscientia Beam. All Rights Reserved. 

 
 
 

 
 
 
 

 

 
 
 
 

https://orcid.org/0000-0001-8177-9666
https://orcid.org/0000-0002-3943-2877
mailto:martinnanje30@gmail.com
mailto:ayub.gitau@uonbi.ac.ke
mailto:duncan.mbuge@uonbi.ac.ke
https://orcid.org/0000-0003-1050-5232
https://www.doi.org/10.18488/cras.v12i2.4598


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178 

© 2025 Conscientia Beam. All Rights Reserved. 

challenges, particularly during the harvesting phase, which notably limits its productivity, profitability, and 

sustainability. 

The harvesting of potatoes in Kenya remains labor-intensive and inefficient, as most smallholder farmers rely on 

traditional methods such as hoes, spades, and manual picking. These practices are not only time-consuming but also 

result in substantial tuber damage and post-harvest losses, which can exceed 20% of the total yield [2]. Such 

inefficiencies adversely impact the quality and marketability of harvested produce, reducing smallholder farmers’ 

income and hampering their capacity to invest in sustainable agricultural practices. The labor-intensive nature of 

manual harvesting poses significant challenges during peak harvest seasons, particularly in regions experiencing 

labor shortages or rising labor costs. These factors underscore the urgent need for innovative mechanized solutions 

tailored to the needs of small-scale farmers. 

Globally, mechanization has been widely recognized as a transformative approach to addressing inefficiencies in 

agricultural production. Studies have shown that mechanized harvesting systems notably enhance operational 

efficiency, minimize crop losses, and improve the quality of harvested produce [3, 4]. Mechanization also shortens 

the harvesting period, reducing the crop’s exposure to adverse weather conditions and other risk factors. In Kenya, 

the adoption of mechanized solutions among smallholder farmers has been notably low. This is largely attributed to 

barriers such as the high costs of machinery, the complexity of operating and maintaining advanced equipment, and 

the lack of locally adaptable technologies [5]. Smallholder farmers often face challenges such as fragmented land 

holdings, limited access to credit facilities, and inadequate technical support, further hindering their ability to adopt 

mechanized harvesting technologies. 

This study addresses these challenges by designing, fabricating, and optimizing a cost-effective single-row potato 

harvester specifically tailored for smallholder farmers in Kenya. The harvester incorporates a V-shaped digging blade, 

a soil-sifting mechanism, and a collection unit powered by a compact gasoline engine; all integrated into a lightweight 

and robust structure. The design prioritizes affordability, ease of use, and adaptability to diverse soil conditions 

prevalent in Kenya, including sandy, clay, loamy, wet, and dry soils. Field trials were carried out in representative 

potato-growing regions to assess the harvester’s performance under varying agroecological conditions. Key 

performance indicators such as harvesting efficiency, tuber damage rate, labor time, and soil contamination were 

evaluated using statistical and regression analysis to provide a comprehensive understanding of the harvester’s 

capabilities and limitations. 

The objectives of this study are threefold: (1) to develop a cost-effective mechanized solution that reduces labor 

time and post-harvest losses, (2) to evaluate the harvester’s performance across diverse soil types using quantitative 

metrics, and (3) to provide actionable recommendations for scaling up production and improving functionality to 

ensure broader adoption by smallholder farmers. By bridging the mechanization gap, this study contributes to the 

overarching goal of sustainable agricultural development, enhancing smallholder farmers’ livelihoods and supporting 

food security initiatives in Kenya and similar contexts. 

This paper outlines the design process, field evaluation results, and performance analysis of the single-row potato 

harvester. It discusses the broader implications of localized mechanization solutions for agricultural productivity, 

rural development, and food security policies, providing valuable insights for stakeholders in the agricultural sector. 

 

2. MATERIALS AND METHODS 

2.1. Study Area and Field Selection 

Field trials for the single-row potato harvester were conducted exclusively in Nyandarua County, one of Kenya’s 

principal potato-producing regions. The trials were undertaken in three representative locations: Ol Kalou (0.27°S, 

36.38°E), Ol Joro Orok (0.23°S, 36.18°E), and Ndaragwa (0.03°S, 36.50°E). These sites were selected to capture the 

county’s characteristic variability in soil conditions, ranging from deep friable loam soils to heavier clay-loam profiles, 

which significantly influence soil–tool interaction dynamics during harvesting [1, 2]. Nyandarua’s cool highland 



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climate and high soil moisture retention make it an ideal testing environment for evaluating harvester performance 

under conditions typical of smallholder potato production systems. 

Test plots measuring 100 m in length and 0.75 m row width were established to align with typical smallholder 

farming systems. Soil characterization was conducted before each trial. Soil moisture was measured at 0–20 cm depth 

using a calibrated TDR probe [6] while soil texture was analyzed using the Bouyoucos hydrometer method [6]. 

Compaction was assessed using a cone penetrometer. These baseline measurements ensured that harvester 

performance could be accurately compared across field conditions. 

The core objective of this study is the development and validation of affordable, efficient agricultural machinery 

for smallholders, a critical need for enhancing food security in low-resource settings [7-10]. This review synthesizes 

the existing research relevant directly to the single-row potato harvester: soil-tool interaction, harvester design 

optimization, and tuber damage assessment. 

 

2.2. The soil-tool interaction and digging mechanics  

Understanding the interaction between soil and the digging implement is fundamental to predicting machine 

performance in the field [11]. The cutting force required to detach and lift soil, denoted as FcF_cFc, is a major 

determinant of draft demand and energy use [12]. Classical models of soil failure and draft force prediction for narrow 

tools underpin the derivation of Equation (1), which guided the design of the digging blade used in this study [13-

15]. 

More recent research highlights the sensitivity of soil–tool behaviour to field variability, particularly shifts in 

moisture and compaction levels [16-19]. These studies emphasize that careful attention to blade geometry can 

improve soil penetration while limiting tuber disturbance [20]. The wide range of soil conditions present in East 

African potato-growing regions reinforces the need for designs that can adapt to differing soil strengths and cohesion 

levels [3, 4]. 

The cutting force was calculated using: 

𝐹 = 𝑐 ∙ 𝑏 ∙ 𝑑 + (
1

2
∙ 𝜌 ∙ 𝑔 ∙ 𝑏 ∙ 𝑑2 𝑡𝑎𝑛 𝛿) + (𝐾𝑝 ∙ 𝛾 ∙ 𝑏 ∙ 𝑑2 tan 𝛽)                                     (1) 

Where: 

𝐹= Cutting force (N). 

𝑐= Soil cohesion (Pa). 

𝑏= Blade width (m). 

𝑑= Cutting depth (m). 

𝜕= Soil’s angle of internal friction (Degrees). 

𝛽= Blade angle (Degrees). 

ρ, g,Kp and γ are assumed constants related to soil bulk density, gravitational acceleration, passive earth pressure 

coefficient and soil weight density, respectively  

 

2.3. Harvester Design, Sifting, and Optimization  

Designing machinery that is practical for smallholder farmers requires a balance between cost, ease of maintenance, 

and portability. Studies consistently show that simple, affordable equipment accelerates adoption in resource-limited 

environments [7, 21-23]. For many farmers, gradual mechanization, progressing from hand tools to intermediate-

scale machinery, helps spread financial risk and encourages long-term use [5, 9, 24]. 

 

2.3.1. Digging Blade design  

The harvester incorporates a V-shaped digging blade intended to penetrate the soil with minimal disruption to 

tubers. The angle and depth of the blade were refined through repeated field adjustments to ensure effective cutting 



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while limiting tuber injury [20]. Equation 1, derived from established soil–tool interaction theories, was used to 

guide these design refinements under the soil conditions present in Nyandarua County [12-15]. Such approaches 

have been documented in agricultural mechanization studies to enhance machine efficiency [4]. 

 

2.3.2. Soil-Sifting Mechanism 

The sifting unit comprised a rectangular sieve plate positioned immediately behind the V-shaped digging blade. 

The structure included a flat steel base plate that tapered into a series of parallel Ø5 mm tines, supported by inclined 

side plates to guide the soil–tuber flow. Once the blade elevated the soil mass, the material advanced onto the sieve, 

where loose soil passed through the tines and the tubers remained on the surface for further separation. 

The vibration frequency (𝜔) of the tines was optimized using the natural frequency formula [25]. 

𝑓 =
1

2𝜋
√

𝑘

𝑚
                             (2)  

Where: 

𝑓= Natural frequency (Hz). 

𝑘= Stiffness of the tine system (N/m). 

𝑚= Effective mass of the tine-soil system (kg). 

The vibrating tines loosened soil and facilitated the separation of tubers while minimizing damage caused by 

mechanical impacts [26, 27]. This mechanism was particularly effective in sandy and loamy soils, where cohesion is 

lower compared to clay soils [3]. 

 

2.3.3.  Fabrication Process 

Fabrication of the harvester was undertaken at the University of Nairobi’s engineering workshop. All 

components were modeled using computer-aided design (CAD) software to ensure precision and compatibility [28-

30]. Steel was used for the main frame to provide durability, while aluminum components were incorporated to reduce 

weight, enhancing portability and usability [20]. The conveyor belt was fabricated using a lightweight, abrasion-

resistant polymer composite to minimize wear during operation. Components were assembled using CNC machining, 

welding, and other precision fabrication techniques. The machine was mounted on rubber-treaded wheels to ensure 

sufficient traction while reducing soil compaction. Preliminary functionality tests were carried out to identify and 

rectify mechanical issues before field deployment. 

 

2.4. Field Trials 

Field trials took place between July and September, during the main potato harvesting period in Nyandarua 

County. The harvester was tested across a range of soil conditions commonly encountered in the region, including 

loamy, clay-loam, dry, and wet field states [16]. Each trial involved harvesting a 100 m row, with three replications 

conducted to allow for statistical comparison [16]. Soil moisture, bulk density, and compaction values were recorded 

prior to each run [4, 19]. 

The same trained operator conducted all trials to maintain operational consistency. Key performance parameters 

including harvesting efficiency, tuber damage, soil contamination, and labour time were recorded for each replicate. 

Informal feedback from local farmers observing the trials helped assess the machine’s practical suitability and ease of 

use. 

 

2.5. Assessment of Tuber Integrity and Harvester Performance 

Tuber quality remains a central concern for farmers because increased damage directly reduces the proportion 

of marketable produce [2, 31, 32]. Mechanical injuries such as bruising, cuts, abrasions, and internal black spots tend 

to occur during soil lifting and the early stages of separation [26, 33-35]. 



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Dynamic impact tests, typically conducted using instrumented spheres, are commonly employed to quantify the 

forces imparted to tubers during harvesting [32, 34, 36]. These analyses rely on established mechanical property 

data for potato tissue, which influence how tubers respond to impact and deformation [27]. Research examining the 

effects of digging blades, conveyors, and separation assemblies provides strong evidence that machine design directly 

influences tuber injury levels [37, 38]. 

The harvester’s performance was assessed using four principal metrics that collectively capture operational 

efficiency, tuber quality, labor requirements, and the effectiveness of the soil-sifting system. 

 

2.5.1. Harvesting Efficiency 

This metric measures the area harvested per unit of time, expressed in square meters per hour (m²/hr). It is 

calculated using the formula. 

𝐻𝑎𝑟𝑣𝑒𝑠𝑡𝑖𝑛𝑔 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑦 =
𝑇𝑜𝑡𝑎𝑙 𝐴𝑟𝑒𝑎 𝐻𝑎𝑟𝑣𝑒𝑠𝑡𝑒𝑑 

𝑇𝑜𝑡𝑎𝑙 𝑇𝑖𝑚𝑒 𝑇𝑎𝑘𝑒𝑛
                                          (3) 

Where: 

Total area harvested is the area covered by the harvester during the test (in square meters or hectares). 

Total time taken is the time spent harvesting (in hours). 

This indicator reflects the harvester’s operational speed and productivity. 

 

2.5.2. Tuber Damage Rate 

To evaluate the quality of the harvested potatoes, the percentage of damaged tubers relative to the total number 

of harvested tubers was calculated as. 

𝑇𝑢𝑏𝑒𝑟 𝐷𝑎𝑚𝑎𝑔𝑒 𝑅𝑎𝑡𝑒 =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑢𝑏𝑒𝑟𝑠 𝐷𝑎𝑚𝑎𝑔𝑒𝑑

𝑇𝑜𝑡𝑎𝑙 𝑁𝑢𝑚𝑏𝑒𝑟 𝑇𝑢𝑏𝑒𝑟𝑠 
𝑋 100                                   (4) 

Where: 

Number of damaged tubers is the count of potatoes that have been damaged during harvesting. 

Total number of tubers is the total number of potatoes harvested during the trial. 

This metric is critical, as higher tuber damage directly reduces the market value and usability of the harvested crop 

[2]. Tuber damage reduces the marketability of the crop, making this an essential metric [26]. 

 

2.5.3. Labor Time Per Unit Area 

The time required to harvest one hectare was calculated to determine the labor-saving potential of the harvester. 

This was done using the formula. 

𝐿𝑎𝑏𝑜𝑢𝑟 𝑇𝑖𝑚𝑒 𝑃𝑒𝑟 𝐻𝑎𝑐𝑡𝑟𝑒 =
𝐿𝑎𝑏𝑜𝑢𝑟 𝑇𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛

𝑇𝑜𝑡𝑎𝑙 𝐴𝑟𝑒𝑎 𝐻𝑎𝑟𝑣𝑒𝑠𝑡𝑒𝑑
                                             (5) 

 

2.5.4. Soil Contamination Rate 

The proportion of harvested potatoes mixed with soil and debris was calculated to assess the effectiveness of the 

soil-sifting mechanism. The formula used was. 

𝑆𝑜𝑖𝑙 𝐶𝑜𝑛𝑑𝑎𝑚𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒 =
𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝐶𝑜𝑛𝑑𝑎𝑚𝑖𝑡𝑒𝑑 𝑇𝑢𝑏𝑒𝑟𝑠

𝑇𝑜𝑡𝑎𝑙 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝐻𝑎𝑟𝑣𝑒𝑠𝑡𝑒𝑑 𝑇𝑢𝑏𝑒𝑟𝑠
𝑋 100                          (6) 

Where: 

The weight of contaminated tubers is the total weight of harvested potatoes with soil or debris attached. 

Total weight of harvested tubers is the total weight of all harvested potatoes. 

 

 



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2.6. Statistical Analysis 

Data analysis was carried out using Python programming software. Descriptive statistics, including mean, 

standard deviation, and variance, were used to summarize the performance metrics. Inferential statistics, such as t-

tests, were employed to compare the harvester’s performance across soil types at a significance level of p=0.05 [4]. 

Regression analysis was used to model the relationships between soil conditions and performance metrics, such as 

harvesting efficiency [16, 19, 28]. 

Regression analysis was performed to explore the relationships between soil types, machine speed, and harvesting 

efficiency. Correlation coefficients were calculated to determine the strength and direction of associations between 

variables such as soil moisture content and tuber damage rate [16]. Visualizations, including histograms, scatter 

plots, and line graphs, were generated using Matplotlib and Seaborn libraries to provide a clear and intuitive 

presentation of the results. 

The formula for the t-test is. 

𝑡 =
�̅�1−�̅�2

√𝑆1
2

𝑛1
+

𝑆2
2

𝑛2

                                             (7) 

Where: 

�̅�1and �̅�2  are the sample means for two groups (e.g., different soil conditions). 

𝑆1
2and 𝑆2

2 are the sample variances for the two groups. 

𝑛1and  𝑛2 are the sample sizes for the two groups. 

This formula tests whether the means of the two groups are notably different, which helps assess whether certain 

variables (e.g., soil type or environmental conditions) affect the harvester's performance. 

 

2.6.1. Coefficient of Variation (CV) 

The coefficient of variation is a measure of the relative variability of data, often used to compare performance 

across trials. It is calculated as: 

𝐶𝑣 =
𝑆

�̅�
𝑋 100                                            (8) 

Where: 

𝑆 is the standard deviation of the dataset. 

�̅�  is the mean of the dataset. 

The CV provides a standardized method for assessing variability, aiding in the comparison of performance across 

different field conditions or trials. 

 

2.6.2. Correlation Coefficient (Pearson’s r) 

To examine relationships between variables, such as between soil conditions and harvesting efficiency, Pearson’s 

correlation coefficient can be calculated. 

𝑟 =
∑(𝑥−�̅�)(𝑦−�̅�)

√∑(𝑥−�̅�)2 ∑(𝑦−�̅�)2
                                  (9) 

Where: 

𝑋 and 𝑌  are the data points for two variables (e.g., soil type and harvesting efficiency). 

�̅�and �̅� are the means of the two variables. 

This formula helps to understand the strength and direction of the linear relationship between two variables. 

 

 

 



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2.6.3. Regression Analysis 

Regression analysis can model the relationship between the harvester's performance and factors such as soil type, 

weather conditions, and machine settings. The simple linear regression equation is. 

𝑌 = 𝛽0 + 𝛽1𝑋 + 휀                                                      (10) 

Where: 

𝑌 is the dependent variable (e.g., harvesting efficiency). 

𝑋 is the independent variable (e.g., soil type or machine speed). 

𝛽0  is the intercept. 

𝛽1  is the slope (the coefficient of the independent variable). 

휀  is the error term. 

This formula helps to model and predict how changes in certain factors affect the harvester's performance. 

 

2.7. Ethical Considerations 

All field trials were carried out with the full consent of participating farmers, who were briefed on the study 

objectives and methods. Farmers were informed of the potential benefits of using the harvester, and their feedback 

was incorporated into evaluating the harvester’s design and functionality [5]. Data collected during the trials were 

anonymized to protect the privacy of participants. Safety measures were implemented during trials to ensure that 

operators and observers were not exposed to any risks. The study adhered to ethical research guidelines [21]. 

Farmers were informed about the study’s objectives and provided consent for participation. Safety protocols were 

implemented during field operations to protect operators and observers from potential risks. 

 

3. RESULTS AND DISCUSSION 

3.1. Design and Fabrication 

The single-row potato harvester was specifically designed to address the unique challenges faced by smallholder 

farmers in Kenya, particularly those with narrow, uneven fields. The compact structure of the harvester (Figure 1) 

enhances maneuverability, enabling it to adapt to various soil types and tuber sizes. This adaptability is crucial in 

smallholder farming, where soil conditions can vary notably from field to field. The harvester was equipped with 

adjustable separator rods (Figure 2), which were instrumental in efficiently separating the tubers from the soil. These 

rods allowed for the optimization of the soil separation process, reducing tuber damage, especially in sandy and loamy 

soils. 

The vibrating digging blade (Figure 3) was a critical innovation in the harvester's design. This feature aimed to 

reduce soil adhesion, which is often a challenge in wet or clayey soils. By loosening the soil and facilitating the smooth 

extraction of tubers, the vibrating blade helped maintain harvesting efficiency while minimizing damage to the tubers. 

The final assembly of the harvester (Figure 4), which integrates the digging blade, separator rods, conveyor, and 

collection unit, demonstrated robustness and durability during field trials. This design provided a comprehensive 

solution for smallholder farmers, offering a machine that could withstand frequent use in tough farm environments, 

aligning with similar studies that highlight the benefits of compact, reliable machinery for small-scale operations [4]. 

 



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Figure 1. Schematic of the single-row potato harvester’s compact structure. 

 

 
Figure 2. Design and configuration of adjustable separator rods. 

 

 
Figure 3. Exploded view of the digging shovel fabricated. 

 



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Figure 4. Fully assembled harvester with integrated components. 

 

3.2. Performance and Design Evaluation of the Potato Harvester 

The harvester’s performance was tested across five soil types: dry, sandy, loamy, clay, and wet. The results 

highlighted the harvester's excellent performance in dry and sandy soils, with harvesting efficiencies of 355.56 m²/hr 

and 300 m²/hr, respectively. These soil types, characterized by loose and granular structures, facilitated smooth 

operation and minimized resistance, enabling the harvester to function optimally. The adjustable separator rods and 

vibrating digging blade played a crucial role in ensuring minimal tuber damage and efficient soil separation in these 

conditions. 

In loamy soils, the harvester achieved a moderate efficiency of 254.55 m²/hr. Despite occasional clumping caused 

by soil cohesion, the harvester’s design proved versatile, showcasing its adaptability to different soil types. Clay soils 

(200 m²/hr) and wet soils (142.86 m²/hr) presented significant challenges, including increased resistance, soil 

adhesion, and compaction. These conditions led to frequent clogging and necessitated cleaning and maintenance. 

These findings align with previous research indicating that soil compaction and moisture content notably impact 

mechanized harvesting performance [5]. Despite these challenges, the harvester consistently outperformed manual 

harvesting, demonstrating its potential to improve productivity for smallholder farmers. 

 

3.3. Harvesting Efficiency 

The harvesting efficiency, a critical performance metric, showed significant variation across soil types (Figure 5). 

Dry soils achieved the highest efficiency at 355.56 m²/hr, followed by sandy soils at 300 m²/hr. These results reflect 

the ease of operation in soils with low moisture content and minimal cohesion, consistent with findings from Visser 

et al. [3] who demonstrated that mechanized harvesters perform best in loose soils. 

In loamy soils, efficiency dropped to 254.55 m²/hr, with occasional delays due to soil clumping. For clay soils 

(200 m²/hr) and wet soils (142.86 m²/hr), efficiency was substantially lower due to increased soil resistance and 

adhesion. These challenges underline the need for further design improvements, particularly for cohesive and high-

moisture soils. 



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Figure 5. Harvesting efficiency across different soil types. 

 

3.4. Tuber Damage Rate 

Tuber damage rate, a critical determinant of marketable yield, varied notably across soil types (Figure 6). Dry 

soils (0.77%) and sandy soils (1.0%) exhibited the lowest damage rates, aligning with industry standards of less than 

2%. The harvester’s effective soil separation and extraction mechanisms ensured minimal bruising and deformation 

in these conditions. 

Damage rates increased in clay soils (3.1%) and wet soils (4.7%), where soil adhesion necessitated higher 

extraction forces, resulting in tuber bruising and deformation. These findings corroborate [4], who noted that 

cohesive soils notably impact tuber integrity during mechanized harvesting. Design modifications, such as adjustable 

blade depth and enhanced soil-sifting components, are necessary to address these challenges and reduce damage in 

challenging soil conditions. 

 

 
Figure 6. Tuber damage rates in various soil conditions. 

 

The soil contamination rate, which reflects the harvester's ability to separate tubers from soil and debris, was 

notably influenced by soil type (Figure 7). Dry soils (0.4%) and sandy soils (0.5%) exhibited minimal contamination, 

consistent with the efficiency of the harvester’s soil separation mechanisms in loose, granular soils. These rates align 

with industrial standards, where contamination rates typically remain below 1% [3]. 



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In contrast, clay soils (2.5%) and wet soils (3.5%) had higher contamination rates due to soil adhesion and 

compaction. These findings highlight the limitations of the current design in high-resistance soils and suggest the 

need for advanced separation mechanisms to reduce contamination in these conditions. 

 

 
Figure 7. Soil contamination rates by soil type. 

 

3.5. Labor Time per Unit Area and Economic Implications 

The labor time per unit area metric demonstrated significant time-saving advantages for the harvester (Figure 

8). In dry soils (4.5 hours/ha) and sandy soils (5 hours/ha), the harvester’s efficient design allowed for uninterrupted 

operation, notably reducing labor time compared to manual harvesting, which typically takes 15–20 hours/ha [1]. 

This reduction translates into lower labor costs and improved productivity, particularly in resource-constrained 

smallholder farming systems. 

in wet soils (10 hours/ha) and clay soils (8.33 hours/ha), the increased resistance and clogging resulted in longer 

harvesting times. These findings highlight the need for design adjustments, such as self-cleaning mechanisms, to 

improve efficiency in challenging conditions. 

 

 
Figure 8. Labor time per hectare across soil types. 

 

 



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3.6. Effect of Soil Texture on Harvesting Performance 

Soil texture had a profound impact on the harvester’s performance. Sandy soils achieved the highest efficiency 

(300 m²/hr) due to their loose structure, which minimized resistance. Loamy soils provided a balance between 

cohesion and granularity, resulting in moderate efficiency (254.55 m²/hr). Clay soils (200 m²/hr) and wet soils (142.86 

m²/hr) posed challenges due to soil adhesion and compaction, necessitating frequent maintenance and adjustments 

(Figure 9). These findings underscore the need for adaptable design features, such as adjustable vibration intensity 

and enhanced soil separation systems, to improve performance across diverse soil types. 

 

 
Figure 9. Effect of soil texture on harvesting performance. 

 

3.7. The Regression Analysis 

The regression analysis in Figure 10 demonstrates a clear relationship between machine speed and harvesting 

efficiency, with performance varying notably based on soil type. In dry and sandy soils, the harvester achieved its 

highest efficiencies, 355.56 m²/hr and 300 m²/hr, respectively. These soil types, characterized by their loose and 

granular structures, provided minimal resistance, allowing the machine to operate at higher speeds without 

compromising functionality or increasing tuber damage. The results underscore the harvester's suitability for low-

resistance soils, highlighting its ability to deliver high productivity in favorable conditions. 

In loamy soils, efficiency peaked at 254.55 m²/hr, with a moderate increase as machine speed rose. The 

intermediate soil cohesion and occasional clumping slightly impeded the harvester’s ability to maintain optimal 

speeds, resulting in a less steep regression curve compared to dry and sandy soils. These findings show that minor 

adjustments, such as improved vibration settings or separator rod configurations, could enhance the harvester’s 

adaptability and performance in loamy conditions. 

For clay and wet soils, the regression curve flattens notably, reflecting limited efficiency gains even with 

increased machine speed. Efficiencies were relatively low at 200 m²/hr for clay soils and 142.86 m²/hr for wet soils. 

The high cohesion and moisture content of these soils caused substantial resistance, leading to frequent interruptions 

for cleaning and adjustments. These challenges align with previous studies that highlight the difficulties mechanized 

harvesters face in cohesive, high-moisture soils due to increased soil adhesion and compaction. 



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Figure 10. Regression model of machine speed versus harvesting efficiency. 

 

3.8. Correlation Between Machine Speed and Harvesting Efficiency 

The regression model (Figure 11) illustrates a positive correlation between machine speed and harvesting 

efficiency. Faster machine speeds resulted in higher efficiencies, particularly in sandy and loamy soils, where lower 

resistance facilitated smooth operation. In clay and wet soils, the increased resistance limited the harvester’s speed, 

reducing efficiency. These findings align with Visser et al. [3], who demonstrated that soil texture notably constrains 

the operational speed of mechanized harvesters. 

 

 
Figure 11. Correlation between soil texture and machine performance. 

 

3.9. Comparison with Other Mechanized Harvesters 

When compared to large-scale industrial harvesters, which typically achieve 400–500 m²/hr in optimal 

conditions, the single-row harvester’s maximum efficiency of 355.56 m²/hr in dry soil was lower. It is important to 

note that this harvester was specifically designed for smallholder farms with limited resources. Despite its lower 

efficiency, the harvester offers a cost-effective solution that notably improves productivity and reduces labor costs 

compared to manual methods, which typically achieve 50–100 m²/hr [1]. 

 

4. CONCLUSION AND RECOMMENDATIONS 

4.1. Conclusion 

This study provided a comprehensive analysis of the performance of the single-row potato harvester across 

various soil types in Kenya, revealing crucial insights into the harvester's functionality and its potential for improving 



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smallholder potato farming. The results highlighted the significant influence of soil texture on critical performance 

indicators such as harvesting efficiency, tuber damage, soil contamination, and labor time. In favorable soil conditions, 

particularly dry and sandy soils, the harvester demonstrated substantial improvements over traditional manual 

harvesting methods. The harvester achieved high operational efficiencies of 355.56 m²/hr in dry soil and 300 m²/hr 

in sandy soil, providing time-saving advantages and reducing labor costs. These conditions also contributed to 

minimal tuber damage rates (0.77% in dry soil and 1.0% in sandy soil), low soil contamination rates (0.4% and 0.5%, 

respectively), and notably reduced labor times (4.5–5 hours/ha), compared to manual harvesting, which typically 

takes 15–20 hours/ha. These findings underscore the harvester's strong potential for enhancing productivity, 

reducing labor costs, and improving farming efficiency in smallholder potato production in regions with favorable 

soil conditions. 

In contrast, wet and clay soils presented significant challenges due to increased soil adhesion, resistance, and soil 

compaction. The harvester’s efficiency dropped considerably in these soil types, with wet soils achieving only 142.86 

m²/hr and clay soils achieving 200 m²/hr. In these conditions, tuber damage rates rose to 4.7% in wet soil and 3.1% 

in clay soil, and the soil contamination rates increased to 3.5% and 2.5%, respectively. Labor times in these soils 

increased to 10 hours/ha in wet soil and 8.33 hours/ha in clay soil, indicating that the harvester’s current design is 

less suited for handling high-moisture and compacted soils. These results highlight the importance of ongoing design 

improvements to address the limitations encountered in such challenging environments. 

The findings emphasize the necessity of localized adaptations in mechanized agricultural equipment. While the 

harvester performed excellently in arid and semi-arid climates with loose soil structures, the study underscores the 

need for design enhancements to optimize its functionality in high-moisture and compacted soils. Future 

improvements, such as the integration of self-cleaning mechanisms, advanced soil separation systems, and adjustable 

components, would enhance the harvester’s performance across all soil types, ensuring its versatility and efficiency 

in various environmental conditions. 

This study also underscores the growing importance of sustainable agricultural machinery that is tailored to 

local farming conditions. By focusing on smallholder farmers in Kenya and other similar regions, the development of 

more efficient, cost-effective, and adaptable harvesting technologies will play a pivotal role in improving the 

sustainability of small- and medium-scale potato farming. Mechanized solutions such as the one proposed in this study 

have the potential to improve not only farming efficiency but also the economic well-being of farmers, contributing 

to greater food security in the region. 

 

4.2. Recommendations for Future Research 

• Optimize the harvester's design for wet and clay soils by improving blade geometry, soil separation, and 

vibration systems. 

• Integrate real-time soil sensors and precision tools for dynamic harvesting control. 

• Conduct expanded field trials in diverse agroecological zones. 

• Analyze long-term durability, maintenance needs, and cost-effectiveness under smallholder conditions. 

 

Funding: This study received no specific financial support. 
Institutional Review Board Statement: The study involved minimal risk and followed ethical guidelines for 
social science fieldwork. Formal approval from an Institutional Review Board was not required under the 
policies of University of Nairobi, Kenya. Informed verbal consent was obtained from all participants, and all 
data were anonymized to protect participant confidentiality. 
Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects 
of the investigation have been omitted, and that any differences from the study as planned have been clarified. 
This study followed all writing ethics. 
Competing Interests: The authors declare that they have no competing interests. 
Authors’ Contributions: All authors contributed equally to the conception and design of the study. All 
authors have read and agreed to the published version of the manuscript. 

 



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