ORIGINAL RESEARCH article

Front. Environ. Sci., 25 August 2026

Sec. Toxicology, Pollution and the Environment

Volume 14 - 2026 | https://doi.org/10.3389/fenvs.2026.1903856

Ecological risk assessment of pesticide residues in diverse farming landscapes of central Zambia

  • 1. Department of Plant Science, School of Agricultural Science, University of Zambia (UNZA), Lusaka, Zambia

  • 2. Department of Agricultural Sciences, Rockview University, Lusaka, Zambia

  • 3. Centre for Agriculture and Bioscience International (CABI) Zambia, Lusaka, Zambia

  • 4. Department of Soil Science, School of Agricultural Science, University of Zambia (UNZA), Lusaka, Zambia

  • 5. Laboratory of Entomology, Plant Sciences Group, Wageningen University, Wageningen, Netherlands

  • 6. School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom

  • 7. Department of Disease Control, School of Veterinary Medicine, University of Zambia, Lusaka, Zambia

  • 8. Africa Center of Excellence for Infectious Diseases of Humans and Animals, University of Zambia, Lusaka, Zambia

  • 9. Department of Paraclinical Studies, School of Veterinary Medicine, University of Zambia, Lusaka, Zambia

  • 10. Soil Physics and Land Management Group, Wageningen University and Research, Wageningen, Netherlands

  • 11. Zambia Agriculture Research Institute, Lusaka, Zambia

  • 12. Faculty of Sciences, University of York, Heslington, United Kingdom

  • 13. Department of Civil Engineering, School of Built Environment, University of Zambia (UNZA), Lusaka, Zambia

  • 14. The Built Environment and Information Technology, Faculty of Engineering, Walter Sisulu University, Eastern Cape, South Africa

Abstract

Agricultural intensification in Zambia has led to widespread reliance on pesticides to safeguard crop yields, yet concerns about their ecological consequences remain insufficiently addressed. This study helps to fill this gap by evaluating the extent and ecological risks associated with pesticide contamination in soils from Chibombo and Mkushi Districts in Central Zambia. We used a two-stage nested research design. First, locations (districts) contained different farming systems (commercial, emergent, and smallholder). Within each farming system, we measured and compared the levels of various pesticide residues, followed by ecological risk assessments (single pollution index and comprehensive pollution index). Pesticide residues were extracted using a modified QuEChERS method and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS). The analysis of pesticide residues indicated that there were significant variations between farming systems in several pesticide residues (acetamiprid, chlorpyriphos, cypermethrin and profenofos). However, there were no significant differences in pesticide residue accumulation between districts. Smallholder farms were generally associated with high insecticide residues (especially chlorpyrifos with a range of 1,494–5,961 μg/kg), emergent farms showed elevated levels of both insecticides and herbicides (notably cypermethrin and fomesafen), while commercial farms were dominated by herbicide residues (acifluorfen, fomesafen, glyphosate and its primary metabolite; aminomethylphosphonic acid). Ecological risk indices indicated significant threats to soil microbial communities, underscoring the potential for long-term disruption of ecosystem services. The data highlights chlorpyrifos and cypermethrin as key pollutants of concern. The study concludes that current pesticide management practices in Central Province pose measurable ecological risks, necessitating urgent policy interventions and adoption of sustainable pest management strategies.

1 Introduction

Across the world, pesticides are widely used to secure agricultural productivity, but their harmful impacts are increasingly documented. Studies show that pesticide residues persist in soils (Wang et al., 2019), contaminate water bodies (Sang et al., 2020; Wei et al., 2021; Sang et al., 2022), and accumulate in food chains (Wang et al., 2013; Shi et al., 2020; Fang et al., 2021), posing risks to human health and biodiversity (Tarazona et al., 2021). The United Nations Environment Programme highlights that excessive and poorly managed pesticide use contributes to ecological degradation, endocrine disruption, and chronic illnesses among exposed populations (UNEP, 2022). Despite international conventions and guidelines, enforcement remains uneven, and many countries continue to struggle with balancing crop protection needs against long-term environmental sustainability. In Sub-Saharan Africa, the harmful use of pesticides is particularly acute (Sheahan et al., 2017). Agricultural intensification has led to rising pesticide consumption, often without adequate training or protective measures for farmers. A systematic review by Fuhrimann et al. (2022) found widespread pesticide residues in soils, water, and food crops across 37 countries in the region, with organochlorines and other highly hazardous pesticides (HHPs) still in use despite bans. Farmers frequently lack access to protective gear, and monitoring systems are weak, resulting in high levels of occupational exposure and ecological contamination. The problem is compounded by limited awareness of safe handling practices and inadequate enforcement of pesticide regulations, making rural communities especially vulnerable to health risks and environmental damage.

Agriculture in Zambia remains heavily dependent on pesticides to safeguard crop yields, particularly in fruit and vegetable production (Siyunda et al., 2022). While these chemicals are vital for pest control, their widespread application has resulted in detectable residues in food crops, raising concerns about food safety and environmental health (Mwanja et al., 2017). Pesticide residues not only pose risks to consumer health but also persist in soils, water bodies, and non-target organisms, thereby disrupting ecological balance (Li et al., 2014; Zhen et al., 2019; Chen et al., 2012). Despite the existence of regulatory frameworks for pesticide lifecycle management, enforcement and monitoring remain limited (MCTI, 2021). This challenge is particularly acute in Central Province of Zambia, where diverse farming landscapes, from smallholder plots to large-scale commercial farms, intensify pesticide use without adequate ecological risk assessment.

Studies in Zambia have focused on pesticide residues in harvested crops and their implications for food safety (FAO, 2022). While these findings are critical, they overlook broader ecological consequences. Residues can accumulate in soils, altering microbial communities and nutrient cycling, which in turn affects soil fertility and long-term sustainability (Mayer et al., 2020; Chen et al., 2022). Aquatic ecosystems are equally vulnerable, as pesticide runoff contaminates rivers and streams, threatening fish populations and aquatic biodiversity essential for local livelihoods (Silva et al., 2017; Raimondo and Forbes, 2022). Pollinators and beneficial insects, crucial for crop production, are also at risk, leading to cascading effects on ecosystem services (Charles et al., 2022). Another gap in the literature lies in geographic coverage. Much of the research has been localized to specific districts, leaving Central Province underrepresented despite its role as the major agricultural hub nationally (Mwanja et al., 2017). Central Province hosts diverse farming systems; smallholder, emergent, and commercial, each with distinct pesticide use patterns and ecological vulnerabilities. Without data from this region, national pesticide management strategies risks are incomplete and poorly tailored to local realities (ZEMA, 2017). Furthermore, most studies adopt a crop-centric or food safety perspective, overlooking cumulative and landscape-level effects of pesticide residues (FAO, 2023). The absence of holistic ecological risk assessment frameworks hinders the development of sustainable pesticide management strategies that balance productivity with environmental integrity (Liu et al., 2013; Shahid et al., 2019; Wan et al., 2025a).

Trophic cascade disruptions constitute a pivotal ecological pathway through which pesticide use intensifies agricultural risks. By diminishing populations of non-target organisms such as predatory insects, pollinators, and aquatic invertebrates, pesticides destabilize food webs and trigger cascading effects across ecosystems (Wan et al., 2025b). The loss of natural predators like ladybird beetles and parasitic wasps often results in pest resurgence, reinforcing chemical dependency and undermining sustainable crop protection (Chen and Li, 2026). Likewise, declines in pollinator abundance reduce fruit and vegetable yields, directly threatening food security and rural livelihoods (Faburé et al., 2025). In aquatic environments, pesticide runoff suppresses insect larvae and small fish populations, which cascades upward to larger fish species critical for nutrition and income (Wan et al., 2026a). Soil microbial communities, essential for nutrient cycling and resilience to climate variability, are also disrupted, weakening long-term fertility. Importantly, cross-continental empirical evidence demonstrates that greater plant diversity suppresses pest populations and reduces reliance on pesticides, offering a natural buffer against these cascading risks (Wan et al., 2026b). These interconnected effects highlight how localized pesticide applications can escalate into landscape-level consequences, particularly in Zambia’s Central Province where diverse farming systems overlap.

This study seeks to address these gaps by conducting an ecological risk assessment of pesticide residues in diverse farming landscapes of Central Province, Zambia. The objectives were (i) to quantify pesticide residues in soils across smallholder, emergent, and commercial farming systems, thereby providing a comprehensive picture of contamination levels, (ii) to evaluate ecological risks posed to non-target organisms and essential ecosystem functions and (iii) to compare residue levels and associated risks across smallholder, emergent, and commercial farming landscapes, highlighting how farming practices and scale influence ecological vulnerability. The study generates evidence-based recommendations to inform sustainable pesticide management and guide policy interventions. By integrating ecological risk assessment into pesticide monitoring, this research aimed to ensure that agricultural productivity is balanced with ecological integrity and long-term environmental health.

2 Materials and methods

2.1 Study area description

The study was conducted in Chibombo and Mkushi Districts of Central Province of Zambia (Figure 1). Both districts are recognized for their substantial pesticide use compared to other districts in the province (Malambo et al., 2019). Chibombo and Mkushi are located in agroecological region IIa of Central Province of Zambia. The region is dominated by luvisols in Mkushi with localised ferralsols while in Chibombo, acrisols are the dominant soil with localized cambisols in the valleys. Agroecological region IIa receives approximately 800–1000 mm of annual rainfall.

FIGURE 1

2.2 Farming landscape description

Central Province encompasses all major farming systems found across Zambia, namely, commercial, emergent, and smallholder farming systems. Commercial farms typically cultivate areas exceeding 20 ha, are highly mechanized, and focus on cash crops such as maize, soybeans, and wheat. These systems rely heavily on agrochemical inputs, including fertilizers and pesticides, and operate year-round through irrigation and advanced machinery. Fertilizer application (Urea and Compound D) ranges between 150–200 kg/ha per cycle, and this is usually done from one cropping cycle to another throughout the year (Moss, 2026). Some commercial farmers practice fallowing after 4–5 years of operation. While modern agronomic practices enhance efficiency, they also intensify pesticide use, elevating risks of soil contamination. Smallholder farms, by contrast, cultivate less than 5 ha, are minimally mechanized, and depend on rainfall, making operations seasonal. Crops are primarily grown for household consumption, and although pesticide use is less intensive, poor handling practices, limited training, and reliance on outdated chemicals still contribute to contamination (Malambo et al., 2019). Emergent farms occupy an intermediate scale (5–20 ha), blending crop production with livestock rearing. This integration promotes resilience through manure recycling, yet pesticide residues disrupt ecosystem balance by entering soils, plants, and livestock feed (Bagayou et al., 2026).

Beyond operational differences, these farming systems also diverge in intrinsic biodiversity. Smallholder farms often maintain heterogeneous crop mixtures and landraces, fostering genetic variability that buffers pest outbreaks through multi-trophic interactions. Emergent farms balance traditional diversity with semi-intensive monocultures, offering moderate resilience. Commercial farms, however, emphasize uniform, high-yield cultivars, reducing biodiversity and heightening vulnerability to pest resurgence, thereby reinforcing chemical dependency. As highlighted by Wan et al. (2022), plant genetic diversity stabilizes trophic interactions and mitigates pest outbreaks, underscoring how biodiversity gradients across farm scales drive divergent pesticide residue profiles.

2.3 Study design

The study employed a two-stage nested research design in which location (districts) was treated as a fixed factor, while farming systems were considered as a random factor nested within locations (Figure 2).

FIGURE 2

2.4 Soil sample collection and pretreatment

Soil sample collection was undertaken during the 2024/2025 farming season in three phases for each sampled site. Phase 1 was between 15 Novermber to 15 December (beginning of the rain season), phase 2 between 15 January to 15 February (mid of rain season) and lastly, phase 3 between 15 March to 15 April (end of rain season). In each district, farms within commercial, emergent and smallholder farming systems that were exposed to 15 years or more of crop production, were randomly selected and soil samples were collected from these farms. A total of 18 sampling sites were identified, 9 per district comprising 3 commercial, 3 emergent, and 3 smallholder farmers. A soil auger (500 mL volume), was used in the soil sampling process. On each farm, three points were selected within the farmer’s soya bean field and 3 samples were obtained from each sampling point at the depth of 0–20cm, within the radius of 10 m to reduce between-sample variations. The 3 soil samples obtained from each sampling point, within the farm were then mixed together and a 500g (fresh weight) sample was taken. Therefore, 3 soil samples from each farm per sampling phase were obtained. All soil samples were transported to the soil chemistry laboratory at the University of Zambia for pretreatment. Soil samples were air dried at ambient temperature, and sieved using a 20-mesh sieve to remove gravel, stones, plant roots and other debris. Thereafter, samples were kept at 4 °C and shipped to Wageningen University for pesticide analysis.

2.5 Pesticides analysis

Pesticide residues were extracted using a modified QuEChERS method and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-tandem mass spectrometry (GC-MS/MS). Glyphosate (GLY) and aminomethylphosphonic acid (AMPA) were extracted using methods similar to the method described by (Yang et al., 2015; Bento et al., 2016). Method validation followed EU SANTE guidelines, with calibration, recovery, precision, and matrix effect checks ensuring accuracy, reproducibility, and reliable quantification (Detailed instrumental conditions and apparatus, and pesticide analysis methodology are provided in the Supplementary Material).

For each quantified pesticide residue, both the single-factor pollution index and the comprehensive pollution index were calculated for every sampling site. To assess overall environmental quality, the study employed the Nemerow multi-factor index as adopted from Zhang and Yan (2012). This method combines the average pollution value derived from the single-factor indices with the maximum concentration of pesticide residues detected at each site, thereby providing a more balanced measure of contamination severity (Zhang et al., 2018). The single-factor index and the comprehensive pollution index were computed using the following formulas;

In Equation 1, Pi represents the environmental quality index for pesticide residue i. Si denotes the evaluation criterion for pesticide residue i (mg/kg), while Ci refers to the quantified concentration of pesticide residue i (mg/kg). In Equation 2, CPI indicates the soil comprehensive pollution index, Pi max is the maximum value among the single-factor pollution indices, and P̅i is the average of the single-factor indices. In this study, ecotoxicological endpoints, expressed as No Observed Effect Concentration (NOEC) values, were employed as reference standards to assess soil environmental quality (Table 1). These values were obtained from standardized toxicity tests using earthworms (Eisenia fetida) as representative bioindicator organisms. Pollution classification followed He et al. (2020): p ≤ 0.7 = non-pollution; 0.7 < p ≤ 1 = slight pollution; 1 < p ≤ 2 = mild pollution; 2 < p ≤ 3 = moderate pollution; and p > 3 = heavy pollution.

TABLE 1

PesticidePrimary source(s)NOEC
AcetamipridFAO/WHO JMPR (2005)5
AcifluorfenUS EPA Ecotoxicology Method (2013)20
ChlorpyrifosFAO/WHO Evaluation Report (2008)1
Clodinafop-propargylFAO/WHO Evaluation Report (2008)50
Lambda-CyhalothrinNPIC; Environmental Chemistry Review0.5
CypermethrinFAO JMPR (2015)1
Emamectin benzoateFAO JMPR (2015)1
FomesafenAgriculture and Environment Research Unit (2026)10
Haloxyfop-methylFAO specifications; AERU PPDB Herbicide20
LufenuronFAO/WHO JMPR (2015)5
ProfenofosFAO/WHO JMPR (2005)5
GlyphosateFAO JMPR (2021)50
AMPA (Glyphosate metabolite)FAO/WHO JMPR (2015)10

Reference Sources and NOECs values for selected pesticides in soil quality assessment.

2.6 Data analysis

Data were pooled from three sampling phases and a nested analysis of variance (ANOVA) was performed and considered statistically significant at α < 0.05 to compare pesticide concentration. All analyses were conducted using SPSS v20, with post hoc tests applied to identify specific differences among farming systems. Homogeneity of variance was tested using Levene’s test, while normality of residuals was assessed through the Shapiro-Wilk test and visual inspection of Q-Q plots. Logarithmic (log10) transformations were used for highly skewed data, while square-root transformations were applied to moderately skewed distributions.

2.7 Method limitations and future outlook

The study’s methods revealed pesticide accumulation patterns but did not fully capture causal chains linking farm type, residues, and soil degradation. Future research should apply advanced statistical approaches, such as structural equation modeling or path analysis proposed by Wang et al. (2025), to strengthen causal inference and guide sustainable agricultural policy.

3 Results

3.1 Descriptive statistics of pesticide concentrations in agricultural soils across different farming landscapes

Tables 24 present descriptive statistics of twelve pesticides and glyphosate’s primary metabolite aminomethylphosphonic acid (AMPA) across farming landscapes. Herbicides such as acifluorfen, clodinafop-propargyl, fomesafen, haloxyfop-methyl, glyphosate, and AMPA showed higher concentrations in commercial farms, with levels ascending from smallholder to emergent to commercial farming systems. High skewness values were observed for acifluorfen (2.45), cypermethrin (2.33), and glyphosate (2.28).

TABLE 2

PesticidesMeanStandard errorStandard deviationSkewnessMinMaxRange
Acetamiprid9.314.069.950.090.0021.0321.03
Acifluorfen2.342.345.732.450.0014.0314.03
Chlorpyrifos2,796.98703.451723.101.621,494.215,961.674,467.46
Clodinafop-propargyl7.421.182.89−0.623.4810.396.91
Cyhalothrin-Lambda3.711.393.401.080.009.639.63
Cypermethrin205.64134.02328.282.3324.44868.839.63
Emamectin benzoate0.000.000.00-0.000.000.00
Fomesafen25.9617.0741.822.140.00108.75108.75
Haloxyfop-methyl71.7013.4432.930.6334.13122.5088.37
Lufenuron3.831.072.62−0.420.007.007.00
Profenofos12.442.756.730.525.6322.8517.22
Glyphosate6.706.7016.402.450.0040.1740.17
AMPA81.6941.56101.801.520.00264.96264.96

Smallholder farming landscapes.

TABLE 3

PesticidesMeanStandard errorStandard deviationSkewnessMinMaxRange
Acetamiprid5.143.318.121.750.0020.3520.35
Acifluorfen3.933.939.632.450.0023.5923.59
Chlorpyrifos1,554.52572.571,4020.7775.023,907.923,907.92
Clodinafop-propargyl3.511.022.51−0.090.006.426.42
Cyhalothrin-Lambda6.142.024.950.491.3012.2010.90
Cypermethrin391.23192.03470.370.8616.291,038.561,022.27
Emamectin benzoate0.000.000.00-0.000.000.00
Fomesafen90.0948.32118.371.250.00290.08290.08
Haloxyfop-methyl60.1916.8941.37−0.070.00111.22111.22
Lufenuron9.404.0910.010.920.0026.0626.06
Profenofos5.271.814.430.540.0012.2612.26
Glyphosate37.368.7621.440.6416.3668.2251.86
AMPA83.7527.7968.070.220.00167.40167.40

Emergent/mixed farming landscapes.

TABLE 4

PesticidesMeanStandard errorStandard deviationSkewnessMinMaxRange
Acetamiprid4.613.548.682.280.0022.0222.02
Acifluorfen266.95254.87624.302.440.001,540.311,540.31
Chlorpyrifos135.8542.92105.130.8133.03303.22270.19
Clodinafop-propargyl29.1729.1771.442.450.00175.00175.00
Cyhalothrin-Lambda27.9916.0141.161.670.00104.65104.65
Cypermethrin43.6028.1168.861.180.00154.42154.42
Emamectin benzoate2.462.165.292.380.0013.1913.19
Fomesafen264.47238.10583.222.442.321,454.241,451.92
Haloxyfop-methyl22.1214.0734.461.010.0071.9971.99
Lufenuron25.5021.4652.562.420.00132.54132.54
Profenofos0.560.360.870.970.001.701.70
Glyphosate175.88108.01264.582.280.00707.71707.71
AMPA440.37167.54410.401.010.001,146.391,146.39

Commercial farming landscapes.

3.2 Pesticide statistical analysis across location and farming landscapes

Statistical analysis reveals no significant differences (P > 0.05) in all the pesticide residue accumulation between the two locations under study (Table 5). However, there were significant differences (P < 0.05) among farming systems in four pesticides; acetamiprid, chlorpyrifos, cypermethrin, and profenofos (Figure 3). In contrast, the other pesticides, including herbicides, did not exhibit significant differences across farming systems. Notably, the data revealed that the accumulation of glyphosate and its primary metabolite (AMPA) was statistically non-significant both across locations and between farming systems. This pattern suggests the widespread and consistent utilization of glyphosate throughout farming landscapes.

TABLE 5

ParameterLocation (P-value)Farming system (P-value)
pH1.000.08
Acetamiprid0.350.04
Acifluorfen0.380.39
Chlorpyrifos0.810.02
Clodinafop Propargyl0.330.53
Cyhalothrine Lambda0.550.13
Cypermethrin0.210.05
Emamectin benzoate0.370.29
Fomesafen0.330.48
Haloxyfop methyl0.350.22
Lufenuron0.420.58
Profenofos0.700.04
Glyphosate0.430.29
AMPA0.830.19

Statistical significance of soil pH and pesticide residues by district and farming system.

bold figures indicate significance across farming systems.

FIGURE 3

The accumulation rate of chlorpyrifos was significantly (P < 0.05) higher than that of all other pesticides across both smallholder and emergent farming systems (Figure 3). Within smallholder farms, concentrations ranged from 1,494 to 5,961 μg/kg, exceeding the permissible limits established by the WHO/FAO in certain locations. Some of the pesticides, such as emamectin benzoate were almost non-detectable across location and farming systems except in a few commercial farming landscapes.

3.3 Environmental quality evaluation using pesticide residue indices in Chibombo and Mkushi

The results presented in Table 6 show clear differences in pesticide residue pollution indices across farming landscapes in Chibombo and Mkushi districts. Chlorpyrifos consistently emerged as the dominant contaminant, with particularly high single pollution index values in smallholder sites in Mkushi SH1 (Pi = 5.962) and SH2 (Pi = 3.608), resulting in comprehensive pollution indices of 4.234 and 2.576 respectively. These values indicate severe contamination compared to Chibombo’s smallholder sites, which recorded moderate pollution levels (CPI = 1.063–1.491). Emergent farms also showed notable contamination, especially Chibombo EF1 (CPI = 2.775) and Mkushi EF2 (CPI = 1.130), largely driven by Chlorpyrifos and cypermethrin residues. In contrast, commercial farms across both districts exhibited minimal contamination, with comprehensive indices ranging from 0.032 to 0.219 in Chibombo and 0.045 to 0.110 in Mkushi. The data highlight chlorpyrifos and cypermethrin as key pollutants of concern, with smallholder farming systems showing higher contamination levels than commercial farming systems in both Districts.

TABLE 6

LocationSampling siteSingle pollution index (Pi)Comprehensive pollution index (CPI)
AcetAcifChlClodCyhCypEmaFomHalLufProfGlyAMPA
ChibomboSH10.000.001.490.000.010.020.000.000.000.000.000.000.011.06
ChibomboSH20.000.002.100.000.000.030.000.000.000.000.000.000.001.49
ChibomboSH30.000.001.950.000.000.060.000.010.010.000.000.000.001.39
MkushiSH10.000.005.960.000.010.150.000.000.000.000.010.000.034.23
MkushiSH20.000.003.610.000.020.870.000.000.010.000.010.000.002.58
MkushiSH30.000.001.670.000.010.110.000.000.000.000.050.000.001.19
ChibomboEF10.000.003.910.000.010.040.000.000.010.000.000.000.022.78
ChibomboEF20.000.000.150.000.000.020.000.020.000.000.000.000.000.11
ChibomboEF30.000.001.850.000.000.070.000.000.000.000.000.000.001.32
MkushiEF10.000.001.760.000.010.250.000.000.010.010.000.000.011.25
MkushiEF20.000.001.580.000.020.940.000.030.000.000.000.000.021.13
MkushiEF30.000.000.080.000.021.040.000.000.000.000.000.000.000.75
ChibomboCF10.000.000.030.000.000.000.000.010.000.000.000.000.000.03
ChibomboCF20.000.000.300.000.080.150.010.000.000.020.000.000.040.22
ChibomboCF30.000.080.210.000.210.110.000.150.000.000.000.000.110.16
MkushiCF10.000.000.070.000.040.000.000.000.000.000.000.000.040.05
MkushiCF20.000.000.060.000.000.000.000.000.000.000.000.010.060.05
MkushiCF30.000.000.150.000.000.000.000.000.000.000.000.000.010.11

Pesticide residue pollution indices for each pesticide per site across location and farming landscape.

Acet; acetamiprid, Acif; acifluorfen-sodium, Chl; chlorpyriphos, Clod; clodinafop-propargyl, Cyh; cyhalothrin lambda, Cyp; cypermethrin, Ema; emamectin benzoate, Fom; fomesafen, Hal; haloxyfop-R-methyl, Luf; lufenuron, Prof; profenofos, Gly; glyphosate, AMPA; aminomethylphosphonic acid.

3.4 Pollution profiles across farming systems in Central Province

The assessment shows that contamination patterns varied strongly by farming systems (Table 7). Commercial sites were largely free from pollution, while emergent farms displayed slight impacts. Smallholder farms contributed most to mild through heavy contamination, underscoring how scale, accessibility, and management practices drive ecological risk across Central Province’s agricultural landscapes.

TABLE 7

Pollution statusNo. sitesFarming systems% of sites
CommercialEmergentSmallholder
Non-pollution761038.90
Slight pollution10106.70
Mild pollution703438.90
Moderate pollution201111.10
Heavy pollution10016.70

Pollution status of farming systems in Chibombo and Mkushi

4 Discussion

The findings from Central Province reveal clear distinctions in pesticide contamination across smallholder, emergent, and commercial farming systems. These differences are not merely quantitative but reflect broader socio-economic, regulatory, and ecological dynamics that shape pesticide use. By situating these results within global research, several critical themes emerge: accessibility and affordability of pesticides, regulatory enforcement, farming scale, and ecological risk.

4.1 Smallholder farming and reliance on organophosphates

Smallholder farms were dominated by the residues of insecticides among the pesticide types under study. The accumulation trend of pesticides in the soils, reflected the application habits among smallholder farmers, who tend to apply more of insecticides than other pesticides such as fungicides and herbicides. The findings in the current study are similar to the findings by Ngowi et al. (2007), who indicated that insecticide use among other pesticides dominated among smallholder vegetable farmers in Northern Tanzania. Similar findings have been reported by Matowo et al. (2020), who indicated more dominant use of lambda-cyhalothrin, cypermethrin and imidacloprid than fungicides and herbicides in rural Tanzania. On the contrary, Okonya et al. (2015), reported a higher utilization of fungicides (72%) among smallholder potato farmers compared to insecticides (62%) and herbicides (3%) in Uganda. The consistency of high insecticide use, compared to herbicides among smallholder farmer in Central Zambia and other developing countries within the sub-Saharan region, can be attributed to the small parcels of land (<5ha) used in their crop production. Smallholder farmers face more insect-pest pressure than weed control, explaining the low use of herbicides compared to insecticides. Notable among the insecticide residues observed, are the alarming levels of chlorpyrifos that exceed risk-based thresholds in the fields of some smallholder farms.

Our results with regards to chlorpyrifos presence in top soils, are similar to several studies that have been conducted in China (Zhu et al., 2014; Tudi et al., 2023). Studies across China have consistently demonstrated the widespread detection of chlorpyrifos in both aquatic and agricultural environments. In marine ecosystems, Zhong et al. (2015) reported chlorpyrifos residues in more than 60% of 72 sediment samples from the Bohai and Yellow Seas, while Chen et al. (2020) found that chlorpyrifos had the highest mean concentrations among pesticides in Shanghai’s aquatic systems, contributing over 50% of the total toxic units. Agricultural soils show similar contamination patterns: Pan et al. (2019) observed ecological risks in northern China farmland, with more than 1% of samples exceeding 0.1 mg/kg; Fu et al. (2020) detected chlorpyrifos in 93.3% of soil samples when assessing 201 pesticide residues; and Han et al. (2017) found chlorpyrifos in 5.3% of nut-cultivated soils across seven provinces, with residue levels ranging from 7.2 to 77.2 μg/kg. Field-specific investigations, such as Wang et al. (2019) in sugarcane soils of Changsha and Danzhou, revealed rapid degradation, with residues declining by over 98% within 35 days and a half-life of approximately 6 days, suggesting limited long-term persistence in that context. Collectively, these findings highlight chlorpyrifos as a pervasive contaminant across diverse ecosystems in China, with concentrations varying by environment and crop type, raising significant ecological concerns despite evidence of rapid breakdown under certain field conditions. The pattern observed in China, reflects findings in Vietnam where reports of chlorpyrifos residues exceeding recommended limits, have been documented (Thuy et al., 2012).

The amount of chlorpyrifos residues detected in most of the studies cited above from China, are far less than the levels detected in some of the smallholder farms in the current study. The dominance of chlorpyrifos in smallholder and emergent farming systems in Central Zambia underscores the reliance of resource-constrained farmers on broad-spectrum, low-cost insecticides. This pattern mirrors findings in South Asia and Sub-Saharan Africa, where organophosphates remain prevalent due to affordability and immediate pest knockdown effects (FAO, 2020). Globally, smallholder reliance on highly hazardous pesticides (HHPs) has been associated with elevated ecological risks, including soil biodiversity loss and contamination of water bodies (Kowalska et al., 2018). The Zambian context aligns with this trend, highlighting the urgent need for targeted interventions such as farmer education, stricter market regulation, and promotion of safer alternatives.

The persistence of extreme chlorpyrifos residues in smallholder fields reflects not only economic reliance on low-cost organophosphates but also a self-reinforcing ecological feedback loop. Repeated applications of broad-spectrum insecticides progressively erode on-farm biodiversity, particularly natural enemies such as parasitoids and predatory arthropods, which ordinary suppress pest populations. As highlighted by Wan et al. (2024), biodiversity loss in agroecosystems triggers cascading social-ecological costs, diminishing biological control intensifies pest outbreaks, compelling farmers to escalate chemical dosages in subsequent seasons. This cycle entrenches chemical dependency, amplifies ecological risk, and accelerates soil and water contamination. In the Zambian context, smallholder farmers cultivating parcels below 5 ha face disproportionate insect-pest pressure, and the absence of functional biodiversity magnifies vulnerability to pest resurgence. Consequently, organophosphate overuse becomes both a symptom and a driver of biodiversity decline, locking farmers into a trajectory of escalating pesticide reliance. The observed dominance of chlorpyriphos residues thus signals more than a contamination issue, it illustrates how ecological simplification undermines resilience, perpetuating hazardous pesticide use, and jeopardizes long-term sustainability of smallholder farming systems. Addressing this cycle requires interventions that restore biodiversity functions such as intercropping and polyculture alongside regulatory and educational measures to reduce dependence on highly hazardous pesticides.

4.2 Emergent/mixed farming and diversification of pesticide use

Emergent farming landscapes indicated a shift toward diversified pesticide use, with cypermethrin and fomesafen becoming more prominent. Comparable findings have been reported in Kenya (Constantine et al., 2020) and Ethiopia (Teklewold et al., 2013), where medium-scale farms increasingly integrate pyrethroids and selective herbicides into their management practices. Similarly, Riwthong et al. (2015), observed that as smallholder farms are transitioning into commercial farms, the change is usually accompanied by an increase in agricultural productivity. Furthermore, their study showed a reduction in the use of traditional methods and an increase in the use of synthetic pesticides in pest management among these farmers. Besides, the transitional/emergent farmers tend to use a large number of pesticide products and mix them together in a bid to effectively control pests. The diversified pesticide uses among emergent farmers in Central Zambia, is reflective of the transitional stage where farmers adopt more specialized chemicals to manage diverse pest and weed pressures.

The relatively consistent application patterns observed in emergent farming systems suggest emerging awareness of application protocols, though variability remains. This echoes studies in Latin America, where medium-scale farms demonstrate improved pesticide handling compared to smallholders but still face challenges in dosage regulation and protective equipment use (Brisbois, 2016).

4.3 Commercial farming and herbicide intensification

Commercial farms in Central Zambia present a markedly different profile, characterized by lower insecticide residues but elevated herbicide concentrations, particularly acifluorfen, fomesafen, and glyphosate. This reflects the global trend of large-scale farms prioritizing weed control through herbicide-intensive strategies (Das et al., 2024). Furthermore, the present findings correspond with those of Karpinski et al. (2026), who reported that many commercial farmers in Germany remain reluctant to adopt strategies aimed at reducing herbicide use due to perceived low economic efficiency. Given that most commercial farms in both districts operate on arable lands exceeding 100 ha, reliance on chemical weed-control methods continues to be favoured over alternative approaches considered less cost-effective.

The widespread use of glyphosate among other herbicides was also observed in the study by Malambo et al. (2019) across 7 provinces of Zambia. Among the herbicides considered in our study, residues of glyphosate and its primary metabolite (AMPA) were higher compared to the residues of other herbicides. These findings are similar to the findings by Silva et al. (2017), who detected glyphosate and its primary metabolite, in 45 percent of 317 topsoil samples collected from ten European countries. Comparable patterns emerge between the two studies, particularly regarding the high frequency and elevated concentrations of AMPA relative to glyphosate across diverse farming landscapes. This consistency among commercial farms suggests prolonged and intensive glyphosate application for weed control. Similarly, in Brazil and Argentina, glyphosate-based herbicides dominate commercial agriculture, with residues frequently detected in soils and water systems (Benbrook, 2016). The uneven contamination profiles, reflected in high skewness values, suggest that while some commercial farms adopt integrated pest management (IPM), others remain heavily dependent on chemical inputs. This variability is consistent with findings from China, where pesticide use among commercial farms varied widely depending on crop type, market access, and regulatory oversight (Pan et al., 2019). Similar heterogeneity has been reported in Argentina by Aparicio et al. (2013) and Primost et al. (2017), in the differential glyphosate residues across farming landscapes.

Commercial farms in Central Zambia, dominated herbicides, exemplify herbicide-intensive management that prioritizes weed control over ecological sustainability. Long-term reliance on glyphosate suppresses wild plant cover, reducing field-scale diversity and weakening biodiversity-mediated pest regulation. As noted in Wan et al. (2024), such biodiversity loss diminishes natural pest suppression and reinforces chemical dependency. Similar outcomes have been documented in Brazil and Argentina, where glyphosate-based monocultures homogenize landscapes and erode resilience (Bøhn and Millstone, 2019). Within Zambia, the dominance of glyphosate and AMPA residues mirrors these global patterns, with uneven contamination profiles suggesting partial but limited adoption of integrated pest management. Collectively, these findings highlight how commercial farms, operating on extensive arable lands, remain locked into herbicide-intensive regimes that degrade biodiversity functions, aligning Zambia’s commercial agriculture with global monoculture trends and underscoring the need for strategies that restore ecological resilience.

4.4 Accessibility and market dynamics

Our study highlights how pesticide accessibility shapes contamination patterns across farming systems. A good example is the price and packaging dynamics of some pesticides such as chlorpyrifos and acifluorfen. Usually, chlorpyrifos is packaged in small containers (≥50 mL) sold at approximately $1 while acifluorfen is usually packaged in much bigger containers (≥1Ltr) and sold at approximately $25, in the Zambian agrochemical market sector. Chlorpyrifos, sold in small, affordable containers, is heavily used by smallholders, while acifluorfen, available only in larger, costlier packages, is concentrated in commercial farms. The trend of pesticide accessibility being influenced by price and packaging dynamics has been revealed in the study by Williamson et al. (2008) across four different African countries. Similar findings have been reported by Barwant et al. (2025), who indicated price sensitivity and limited availability of sustainable products as part of the major barriers to sustainable pest management. Furthermore, this dynamic is echoed in Botswana and Zimbabwe, where pesticide packaging size and price strongly influence farmer choices, often leading to disproportionate use of cheaper, more hazardous chemicals (FAO, 2020).

Such market-driven disparities emphasize the need for regulatory frameworks that not only restrict highly hazardous pesticides (HHPs) but also promote equitable access to safer alternatives. Subsidies for biopesticides or integrated pest management tools could reduce reliance on hazardous chemicals among smallholders.

4.5 Ecological and health risks

The concentrations of chlorpyrifos detected in some farms exceed those reported in many previous investigations, where even lower levels have been shown to cause adverse ecological and health effects. For instance, Chen et al. (2020) identified chlorpyrifos as the dominant contributor to aquatic toxicity in Shanghai, at levels lower than those observed in the current study. Its persistence is particularly concerning given its WHO classification as moderately hazardous (Class II) and its global association with pollinator decline, aquatic toxicity, and human health risks such as neurotoxicity.

Evidence further shows that chlorpyrifos and cypermethrin residues trigger cascading ecological damage across multiple trophic levels. As highlighted in Wan et al. (2025b) and Wan et al. (2026b), repeated exposure suppresses soil fauna, disrupts nutrient cycling, and reduces wild pollinator populations, leading to irreversible losses of ecosystem services such as natural pest regulation and soil fertility. Smallholder systems are disproportionately vulnerable due to limited access to modern inputs, extension services, and pollution control technologies. The heavy contamination observed reflects systemic issues including overuse of broad-spectrum pesticides, poor waste management, and lack of farmer training. Similar patterns have been documented across Africa, where smallholders often lack protective equipment and knowledge of safe application practices, increasing exposure risks for farmers and their households (Matowo et al., 2020).

The current study therefore highlights that ecological degradation and human health risks are unevenly distributed across farming systems, with smallholders most at risk. Addressing these challenges requires enhanced monitoring, differentiated interventions, and adoption of sustainable pest management practices to safeguard both ecosystems and public health.

5 Conclusion and recommendation

The findings of this study demonstrate clear differentiation across Zambia’s farming systems. Smallholder farms are mostly associated with high insecticide residues, particularly chlorpyrifos. Emergent farms show elevated levels of both insecticides and herbicides, notably cypermethrin and fomesafen, while commercial farms are dominated by herbicide residues such as acifluorfen, fomesafen, glyphosate, and its metabolite AMPA. These contrasts reflect variations in farming scale and pest management strategies. From an environmental and health perspective, the persistence of chlorpyrifos and cypermethrin in smallholder and emergent systems is concerning due to their toxicity, while the heavy reliance on glyphosate-based herbicides in commercial estates raises issues of ecological degradation and resistance development. To address these risks, differentiated diversification strategies tailored to Zambia’s three farming systems are recommended. For smallholders, intercropping and polyculture can reduce insecticide dependency while enhancing resilience. Emergent mixed farms would benefit from integrated crop-livestock systems that strengthen biodiversity regulation and mitigate dual pesticide loads. For commercial estates, cover crop rotation offers a pathway to reduce herbicide reliance and restore ecological functions. Finally, to quantify the causal pathways linking farming scale, pesticide residue loads, biodiversity decline, and ecosystem service degradation, the construction of a coupled path analysis model is proposed. Such a model would provide a robust framework for evidence-based regulation, farmer training, and the promotion of safer alternatives, thereby balancing agricultural productivity with sustainability and public health protection.

Statements

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

AS: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – original draft. DL: Supervision, Writing – review and editing. NM: Formal Analysis, Supervision, Validation, Writing – review and editing. JY: Supervision, Validation, Writing – review and editing. AG: Investigation, Writing – review and editing. HT: Investigation, Writing – review and editing. SS: Writing – review and editing. RQ: Writing – review and editing. MS: Writing – review and editing. LM: Investigation, Writing – review and editing. XY: Writing – review and editing. RO: Methodology, Writing – review and editing. LF: Writing – review and editing. VN: Methodology, Writing – review and editing. AD: Supervision, Validation, Writing – review and editing. EM: Funding acquisition, Project administration, Supervision, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the FoSTA-Health project, funded by the European Commission (EC)’s Horizon Europe programme (project number 101060887) and the United Kingdom Research and Innovation (UKRI). The content of this research represents the views of the author (s) only and is their sole responsibility. The European Research Executive Agency (REA) and the EC are not responsible for any information that it contains.

Acknowledgments

This work could not have been completed without the support of our collaborators at the University of Zambia (UNZA), Wageningen University and University of Leeds.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2026.1903856/full#supplementary-material

References

  • 1

    Agriculture and Environment Research Unit (AERU) (2026). Pesticide Properties Database (PPDB). Hatfield, Hertfordshire, United Kingdom: Agriculture and Environment Research Unit (AERU), University of Hertfordshire. Available online at: https://sitem.herts.ac.uk/aeru/ppdb (Accessed April 2, 2026).

  • 2

    AparicioV. C.De GeronimoE.MarinoD.PrimostJ.CarriquiribordeP.CostaJ. L. (2013). Environmental fate of glyphosate and aminomethylphosphonic acid in surface waters and soil of agricultural basins. Chemosphere93, 18661873. 10.1016/j.chemosphere.2013.06.041

  • 3

    BagayouA.HamdacheA.DianeY.EzziyyaniM. (2026). Study of ecotoxicological risks related to the use of pesticides on soil organisms in the loukkos area (larache, Morocco). Euro-Mediterr J. Environ. Integr.11, 26. 10.1007/s41207-025-01005-8

  • 4

    BarwantM. M.OgidiO. I.YogitaC.MunjeR. (2025). “Study of consumers choices on pesticides use and sustainability,” in The Interplay of Pesticides and Climate Change. Editors BabaniyiB. R.BabaniyiE. E. (Cham: Springer). 10.1007/978-3-031-81669-7_18

  • 5

    BenbrookC. M. (2016). Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur.28 (3), 115. 10.1186/s12302-016-0070-0

  • 6

    BentoC. P. M.YangX.GortG.XueS.van DamR.ZomerP.et al (2016). Persistence of glyphosate and aminomethylphosphonic acid in loess soil under different combinations of temperature, soil moisture and light/darkness. Sci. Total Environ.572, 301311. 10.1016/j.scitotenv.2016.07.215

  • 7

    BøhnT.MillstoneE. (2019). The introduction of thousands of tonnes of glyphosate in the food chain—an evaluation of glyphosate tolerant soybeans. Foods8 (12), 669. 10.3390/foods8120669

  • 8

    BrisboisB. (2016). Bananas, pesticides and health in Southwestern Ecuador: a scalar narrative approach to targeting public health responses. Soc. Sci. and Med.150, 184191. 10.1016/j.socscimed.2015.12.026

  • 9

    CharlesS.RatierA.BaudrotV.MultariG.SiberchicotA.WuD.et al (2022). Taking full advantage of modelling to better assess environmental risk due to xenobiotics—The all-in-one facility MOSAIC. Environ. Sci. Pollut. Control Ser.29 (20), 2924429257. 10.1007/s11356-021-15042-7

  • 10

    ChenS.LiZ. (2026). Tracing pesticides through terrestrial food webs with wildlife at risk. iScience29 (6), 115870. 10.1016/j.isci.2026.115870

  • 11

    ChenC.QianY. Z.LiuX. J.TaoC. J.LiangY.LiY. (2012). Risk assessment of chlorpyrifos on rice and cabbage in China. Regul. Toxicol. Pharmacol.62, 125130. 10.1016/j.yrtph.2011.12.011

  • 12

    ChenC.ZouW. B.CuiG. L.TianJ. C.WangY. C.MaL. M. (2020). Ecological risk assessment of current-use pesticides in an aquatic system of shanghai, China. Chemosphere257, 127222. 10.1016/j.chemosphere.2020.127222

  • 13

    ChenD.HaoG.SongB. (2022). Finding the missing property concepts in pesticide-likeness. J. Agric. Food Chem.70, 1009010099. 10.1021/acs.jafc.2c02757

  • 14

    ConstantineK. L.KansiimeM. K.MugambiI.NundaW.ChachaD.RwareH.et al (2020). Why don't smallholder farmers in Kenya use more biopesticides?Pest Manag. Sci.76, 36153625. 10.1002/ps.5896

  • 15

    DasT. K.BeheraB.NathC. P.GhoshS.SenS.RajR.et al (2024). Herbicides use in crop production: an analysis of cost-benefit, non-target toxicities and environmental risks. Crop Prot.181, 106691. 10.1016/j.cropro.2024.106691

  • 16

    FaburéJ.HeddeM.Le PerchecS.PesceS.SucréE.FritschC. (2025). Role of trophic interactions in transfer and cascading impacts of plant protection products on biodiversity: a literature review. Environ. Sci. Pollut. Res.32, 29933031. 10.1007/s11356-024-35190-w

  • 17

    FangL.LiaoX. F.ZhangQ.ShiL. C.ZhouL. D.ZhaoH. P.et al (2021). An orthogonal experimental design and QuEChERS based UFLC-MS/MS for multi-pesticides and human exposure risk assessment in honeysuckle. Ind. Crops Prod.164, 113384. 10.1016/j.indcrop.2021.113384

  • 18

    FAO (2020). Shortlisting of Hhps - Botswana, Zambia. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO), 17.

  • 19

    FAO (2022). Pesticides Use, Pesticides Trade and Pesticides Indicators– Global, Regional and Country Trends, 1990–2020. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO). Available online at: https://openknowledge.fao.org/handle/20.500.14283/cc0918en (Accessed April 11, 2026).

  • 20

    FAO (2023). Understanding the context, Pest and pesticide management. Food Agric. Organ. U. N. | IPM Pesticide Risk Reduct. Food Agric. Organ. U. N.Available online at: https://www.fao.org/pestand-pesticide-management/about/understanding-the-context/en/.

  • 21

    Food and Agriculture Organization of the United Nations and World Health Organization. (FAO/WHO JMPR) (2005). Pesticide Residues in Food – 2005: Report of the Joint Meeting of the FAO Panel of Experts on Pesticide Residues in Food and the Environment and the WHO Core Assessment Group on Pesticide Residues, Geneva, Switzerland, 20–29 September 2005 (FAO Plant Production and Protection Paper No. 183). Rome: FAO.

  • 22

    Food and Agriculture Organization of the United Nations and World Health Organization (FAO/WHO Evaluation Report) (2008). Pesticide Residues in Food – 2008: Report of the Joint Meeting of the FAO Panel of Experts on Pesticide Residues in Food and the Environment and the WHO Core Assessment Group on Pesticide Residues. Rome: FAO.

  • 23

    Food and Agriculture Organization of the United Nations and World Health Organization (FAO/WHO JMPR) (2015). Pesticide Residues in Food – 2015: Report of the Joint Meeting of the FAO Panel of Experts on Pesticide Residues in Food and the Environment and the WHO Core Assessment Group on Pesticide Residues. Rome: FAO.

  • 24

    Food and Agriculture Organization of the United Nations and World Health Organization. (FAO/WHO JMPR) (2021). Pesticide Residues in Food – 2021. Rome: FAO.

  • 25

    FuY. W.DouX. W.LuQ.QinJ. A.LuoJ. Y.YangM. H. (2020). Comprehensive assessment for the residual characteristics and degradation kinetics of pesticides in Panax notoginseng and planting soil. Sci. Total Environ.714, 136718. 10.1016/j.scitotenv.2020.136718

  • 26

    FuhrimannS.WanC.BlouzardE.VeludoA.HoltmanZ.Chetty-MhlangaS.et al (2022). Pesticide research on environmental and human exposure and risks in Sub-Saharan Africa: a systematic literature review. Int. J. Environ. Res. Public Health19 (1), 259. 10.3390/ijerph19010259

  • 27

    HanY. X.MoR. H.YuanX. Y.ZhongD. L.TangF. B.YeC. F.et al (2017). Pesticide residues in nut-planted soils of China and their relationship between nut/soil. Chemosphere180, 4247. 10.1016/j.chemosphere.2017.03.138

  • 28

    HeH.ShiL.YangG.YouM.VasseurL. (2020). Ecological risk assessment of soil heavy metals and pesticide residues in tea plantations. Agriculture10, 47. 10.3390/agriculture10020047

  • 29

    KowalskaJ. B.MazurekR.GasiorekM.ZaleskiT. (2018). Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination–A review. Environ. Geochem. Health.40, 23952420. 10.1007/s10653-018-0106-z

  • 30

    LiH. Z.WeiY. L.LydyM. J.YouJ. (2014). Inter-compartmental transport of organophosphate and pyrethroid pesticides in south China: implications for a regional risk assessment. Environ. Pollut.190, 1926. 10.1016/j.envpol.2014.03.013

  • 31

    LiuC.SiblyR. M.GrimmV.ThorbekP. (2013). Linking pesticide exposure and spatial dynamics: an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes. Ecol. Model.248, 92102. 10.1016/j.ecolmodel.2012.09.016

  • 32

    MalamboM. J.MukangaM.NyirendaJ.KabambaB.SalatiR. K. (2019). Knowledge and practice of pesticides use among small holder farmers in Zambia. Int. Journal Hortic. Agric. Food Sciencevol-3 (Issue-4), 184190. 10.22161/ijhaf.3.4.5

  • 33

    MatowoN. S.TannerM.MunhengaG.MapuaS. A.FindaM.UtzingerJ.et al (2020). Patterns of pesticide usage in agriculture in rural Tanzania call for integrating agricultural and public health practices in managing insecticide-resistance in malaria vectors. Malar. J.19, 257. 10.1186/s12936-020-03331-4

  • 34

    MayerM.DuanX.SundeP.ToppingC. J. (2020). European hares do not avoid newly pesticide-sprayed fields: overspray as unnoticed pathway of pesticide exposure. Sci. Total Environ.715, 136977. 10.1016/j.scitotenv.2020.136977

  • 35

    MCTI (2021). Zambia Agribusiness and Trade Project (ZATP) – Pest Management Plan. Zambia: Ministry of Commerce, Trade and Industry (MCTI), Government of the Republic of Zambia.

  • 36

    MossB. (2026). Zambia’s path to fertilizer self-sufficiency. Agri Focus Afr. Mark.

  • 37

    MwanjaM.JacobsC.MbeweA. R.MunyindaS. N. (2017). Assessment of pesticide residue levels among locally produced fruits and vegetables in monze district, Zambia. Int. J. Food Contam.4 (11), 11. 10.1186/s40550-017-0056-8

  • 38

    National Pesticide Information Center (NPIC) (2026). Pesticide Fact Sheets and Environmental Chemistry Reviews. Oregon State University and U.S. EPA. Available online at: http://npic.orst.edu (Accessed April 8, 2026).

  • 39

    NgowiA. V. F.MbiseT. J.IjaniA. S. M.LondonL.AjayiO. C. (2007). Smallholder vegetable farmers in northern Tanzania: pesticides use practices, perceptions, cost and health effects, Crop Prot., 26, 11, 16171624. 10.1016/j.cropro.2007.01.008

  • 40

    OkonyaJ. S.KroschelJürgenA. (2015). Cross-sectional study of pesticide use and knowledge of smallholder potato farmers in Uganda, BioMed Res. Int., 2015, 9. 10.1155/2015/759049

  • 41

    PanL. X.FengX. X.CaoM.ZhangS. W.HuangY. F.XuT. H.et al (2019). Determination and distribution of pesticides and antibiotics in agricultural soils from northern China. RSC Adv.9, 1568615693. 10.1039/C9RA00783K

  • 42

    PrimostJ. E.MarinoD. J. G.AparicioV. C.CostaJ. L.CarriquiribordeP. (2017). Glyphosate and AMPA, “pseudo-persistent” pollutants under real world agricultural management practices in the mesopotamic pampas agroecosystem, Argentina. Environ. Pollut.229, 771779. 10.1016/j.envpol.2017.06.006

  • 43

    RaimondoS.ForbesV. E. (2022). Moving beyond risk quotients: advancing ecological risk assessment to reflect better, more robust and relevant methods. Ecol. (Brunoy)3 (2), 145160. 10.3390/ecologies3020012

  • 44

    RiwthongS.SchreinemachersP.GrovermannC.BergerT. (2015). Land use intensification, commercialization and changes in Pest management of smallholder upland agriculture in Thailand. Environ. Sci. and Policy45, 1119. 10.1016/j.envsci.2014.09.003

  • 45

    SangC. H.SorensenP. B.AnW.AndersenJ. H.YangM. (2020). Chronic health risk comparison between China and Denmark on dietary exposure to chlorpyrifos. Environ. Pollut.257, 113590. 10.1016/j.envpol.2019.113590

  • 46

    SangC. H.YuZ. Y.AnW.SorensenP. B.JinF.YangM. (2022). Development of a data driven model to screen the priority control pesticides in drinking water based on health risk ranking and contribution rates. Environ. Int.158, 106901. 10.1016/j.envint.2021.106901

  • 47

    ShahidN.LiessM.KnillmannS. (2019). Environmental stress increases synergistic effects of pesticide mixtures on Daphnia magna. Environ. Sci. Technol.53 (21), 1258612593. 10.1021/acs.est.9b04293

  • 48

    SheahanM.BarrettC. B.GoldvaleC. (2017). Human health and pesticide use in Sub-Saharan Africa. Agric. Econ. (United Kingdom)48, 2741. 10.1111/agec.12384

  • 49

    ShiR. G.YuanL.ChenM. L.ZhengX. Q.LiuX. W.ZhaoY. J.et al (2020). Detection of frequently used pesticides in apple orchard soil in China by high resolution mass spectrometry. Pol. J. Environ. Stud.29, 13411350. 10.15244/pjoes/108509

  • 50

    SilvaV.MolH. G. J.ZomerP.TienstraM.RitsemaC. J.GeissenV. (2017). Pesticide residues in European agricultural soils – a hidden reality unfolded. Sci. Total Environ.653, 15321545. 10.1016/j.scitotenv.2018.10.441

  • 51

    SiyundaA. C.MwilaM. N.MwalaM.MunyindaK.KamfwaK.ChipabikaG.et al (2022). Screening for resistance to cowpea aphids (Aphis craccivora koch.) in mutation derived and cultivated cowpea (Vigna unguiculata L. walp.) genotypes. Int. J. Sci. Bus.13 (1), 1526. 10.5281/zenodo.6614442

  • 52

    SiyundaA. C.LunguD.MwilaN. M.YengweJ.GanatraA. A.TripathiH.et al (2026). Heavy metal and pesticide toxicity in agricultural soils across a farming intensity gradient in Central Zambia. Front. Soil Sci.6, 1903905. 10.3389/fsoil.2026.1903905

  • 53

    TarazonaD.TarazonaG.TarazonaJ. V. (2021). A simplified population-level landscape model identifying ecological risk drivers of pesticide applications, part one: case study for large herbivorous mammals. Int. J. Environ. Res. Publ. Health18 (15), 15. 10.3390/ijerph18157720

  • 54

    TeklewoldH.KassieM.ShiferawB.KöhlinG. (2013). Cropping system diversification, conservation tillage and modern seed adoption in Ethiopia: impacts on household income, agrochemical use and demand for labor. Ecol. Econ.93 (2013), 8593. 10.1016/j.ecolecon.2013.05.002

  • 55

    ThuyP. T.Van GeluweS.NguyenV. A.Van der BruggenB. (2012). Current pesticide practices and environmental issues in Vietnam: management challenges for sustainable use of pesticides for tropical crops in (South-East) Asia to avoid environmental pollution. J. Mat. Cycles Waste Manag.14, 379387. 10.1007/s10163-012-0081-x

  • 56

    TudiM.YangL.WangL.LvJ.GuL.LiH.et al (2023). Environmental and human health hazards from chlorpyrifos, pymetrozine and avermectin application in China under a climate change scenario: a comprehensive review. Agriculture13, 1683. 10.3390/agriculture13091683

  • 57

    UNEP (2022). Synthesis Report on the Environmental and Health Impacts of Pesticides and Fertilizers and Ways to Minimize them. Nairobi, Kenya: United Nations Environment Programme (UNEP), through the National Pesticide Information Center initiative.

  • 58

    United States Environmental Protection Agency (USEPA) (2013). Ecotoxicology Test Guidelines: OCSPP Harmonized Test Guidelines. Washington, DC: U.S. EPA.

  • 59

    WanN. F.FuL.DaineseM.HuY. Q.KiærL. P.IsbellF.et al (2022). Plant genetic diversity affects multiple trophic levels and trophic interactions. Nat. Commun.13, 7312. 10.1038/s41467-022-35087-7

  • 60

    WanN.DaineseM.WangY.LoreauM. (2024). Cascading social-ecological benefits of biodiversity for agriculture. Curr. Biol.34 (12), R587R603. 10.1016/j.cub.2024.05.001

  • 61

    WanN. F.WoodcockB. A.ScherberC.WyckhuysK. A. G.LiZ.And Xuhong QianX. (2025a). Leaving synthetic pesticides behind. Science388 (6748), 712713. 10.1126/science.adv7806

  • 62

    WanN. F.FuL.DaineseM.KiærL. P.HuY. Q.XinF.et al (2025b). Pesticides have negative effects on non-target organisms. Nat. Commun.16, 1360. 10.1038/s41467-025-56732-x

  • 63

    WanN. F.ShenS.LiM.ChenY.PanF. Y.ChenX.et al (2026a). Trophic cascades drive sustainability in the agricultural heritage rice-fish coculture system. Current biology. Curr. Biol.36, 22072221. 10.1016/j.cub.2026.03.046

  • 64

    WanN. F.WangY. Q.FuL.LiuJ.WoodcockB. A.HuY. Q.et al (2026b). Global evidence that plant diversity suppresses pests and promotes plant performance and crop production. Nat. Ecol. Evol.10, 293307. 10.1038/s41559-025-02964-5

  • 65

    WangS. M.WangZ. L.ZhangY. B.WangJ.GuoR. (2013). Pesticide residues in market foods in Shaanxi Province of China in 2010. Food Chem.138, 20162025. 10.1016/j.foodchem.2012.11.116

  • 66

    WangH. P.ZhengL. G.YuW. T.CaoX. M.YangR. B. (2019). Dissipation behavior of chlorpyrifos residues and risk assessment in sugarcane fields. Biomed. Chromatogr.33, e4424. 10.1002/bmc.4424

  • 67

    WangY. Q.ShiD. P.ScherberC.WoodcockB. A.HuY. Q.WanN. F. (2025). Understanding biodiversity effects on trophic interactions with a robust approach to path analysis. Cell. Rep. Sustain.2 (5), 100362. 10.1016/j.crsus.2025.100362

  • 68

    WeiG. L.WangC.NiuW. P.HuanQ.TianT. T.ZouS. J.et al (2021). Occurrence and risk assessment of currently used organophosphate pesticides in overlying water and surface sediments in guangzhou urban waterways, China. Environ. Sci. Pollut. Res.28, 4819448206. 10.1007/s11356-021-13956-w

  • 69

    WilliamsonS.AndrewA.PrettyJ. (2008). Trends in pesticide use and drivers for safer Pest management in four African countries. Crop Prot.27 (10), 13271334. 10.1016/j.cropro.2008.04.006

  • 70

    YangX.WangF.BentoC. P. M.XueS.GaiL.van DamR.et al (2015). Short-term transport of glyphosate with erosion in Chinese loess soil - a flume experiment. Sci. Total Environ.512–513, 406414. 10.1016/j.scitotenv.2015.01.071

  • 71

    Zambia Environmental Management Agency (ZEMA) (2017). Zambia Environment Outlook Report. Available online at: http://www.zema.org.zm/index.php/download/zambia-environment-outlook-report-3/ (Accessed April 12, 2026).

  • 72

    ZhangY. L.YanT. Z. (2012). Application of nemerow index method in the evaluation of soil heavy metal pollution. J. Henan Inst. Educ.2, 3539.

  • 73

    ZhangQ.FengM.HaoX. (2018). Application of nemerow index method and integrated water quality index method in water quality assessment of zhangze reservoir. IOP Conf. Ser. Earth Environ. Sci.128, 012160. 10.1088/1755-1315/128/1/012160

  • 74

    ZhenX. M.LiuL.WangX. M.ZhongG. C.TangJ. H. (2019). Fates and ecological effects of current-use pesticides (CUPs) in a typical river-estuarine system of Laizhou Bay, north China. Environ. Pollut.252, 573579. 10.1016/j.envpol.2019.05.141

  • 75

    ZhongG. C.TangJ. H.XieZ. Y.MiW. Y.ChenY. J.MollerA.et al (2015). Selected current-use pesticides (CUPs) in coastal and offshore sediments of bohai and yellow seas. Environ. Sci. Pollut. Res.22, 16531661. 10.1007/s11356-014-2648-7

  • 76

    ZhuZ. Y.WangK.ZhangB. (2014). Applying a network data envelopment analysis model to quantify the eco-efficiency of products: a case study of pesticides. J. Clean. Prod.69, 6773. 10.1016/j.jclepro.2014.01.064

Summary

Keywords

chlorpyrifos, ecological risk assessment, farming systems, glyphosate, pesticide residues, soil contamination, sustainable pest management

Citation

Siyunda AC, Lungu D, Mwila NM, Yengwe J, Ganatra AA, Tripathi H, Sait SM, Quinnell RJ, Simuunza M, Moonga L, Yang X, Osman R, Fleskens L, Nkhoma V, Dougill A and Mwanaumo E (2026) Ecological risk assessment of pesticide residues in diverse farming landscapes of central Zambia. Front. Environ. Sci. 14:1903856. doi: 10.3389/fenvs.2026.1903856

Received

09 June 2026

Revised

10 July 2026

Accepted

24 July 2026

Published

25 August 2026

Volume

14 - 2026

Edited by

Joginder Singh, Nagaland University, India

Reviewed by

Daniel Brice Nkontcheu Kenko, University of Buea, Cameroon

Nian-Feng Wan, Shenzhen Research Institute of East China University of Science and Technology, China

Updates

Copyright

*Correspondence: Aaron C. Siyunda,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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