Evaluation of pesticide reduction strategies in cropping systems. A review

Evaluation of pesticide reduction strategies in cropping systems. 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A review Han Zhang, David Makowski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722542/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Agronomy for Sustainable Development → Version 1 posted You are reading this latest preprint version Abstract The unsustainable and excessive use of pesticides in agriculture causes multi-faceted problems worldwide. Numerous strategies are proposed to reduce the intensity of pesticides; however, the knowledge regarding their performances is scattered. To move forward, it is crucial to identify efficient pesticide reduction strategies. For the first time, based on a systematic review of ten secondary literatures, we reviewed 97 primary literatures reporting 135 studies assessing pesticide reduction strategies. Sixteen types of individual strategy were identified, among which the most investigated strategies were site-specific pesticide application, physical/mechanical control, and advanced machinery. Herbicide was the most studied pesticide group regarding pesticide reduction. The most frequent method considered to assess pesticide reduction strategies was field experiment (83%), followed by lab experiment, interview, and modelling (5% each), and human health study (2%). Fifty-one indicators reflecting the impacts of pesticide reduction strategies were collected and grouped into four impact categories: economic output, pest control efficacy, pesticide related input, and side effect (non-target species, human health, and the environment). Based on generalized linear mixed-effects models, we found that pesticide reduction strategies had higher chance to reduce pesticide input and side effects than to improve economic output and pest control efficacy. Through topic modelling analysis, we were able to allocate the primary literatures to two main topics: topic 1 includes weed management in cereals, and topic 2 is about insect and disease management in fruits. Studies focusing on topic 2 and studies combining several pesticide reduction strategies had higher probabilities to report positive effects from pesticide reduction strategies than topic 1 and single strategies respectively. We also found that organic system produces more positive results than low-input system, especially regarding side effects. Agronomy sustainable agriculture pesticide reduction strategies systematic review topic modelling organic system low-input system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Sixty years after the iconic book Silent Spring , synthetic pesticides are still at the heart of major environmental burdens (Carson 1962). Largely due to the intensification of pesticide use, the global pesticide consumption grew 20% by volume over the last decade (Shattuck et al. 2023). Pesticides continue to be detrimental toward biodiversity (e.g., insects, birds, wild plants) (Serrão et al. 2022; Wan et al. 2025; Jiménez-Peñuela et al. 2025), human health (Shekhar et al. 2024; Zhou et al. 2025), and the environment (e.g., water, soil, air) (Tudi et al. 2021; Elumalai et al. 2025). The indiscriminate use of pesticides also causes pest resistance, target pest resurgence, and second pest outbreaks, resulting in risks of insufficient pest management and financial losses for farmers (Hawkins et al. 2019; Sánchez-Bayo 2021). To tackle the unsustainable use of pesticides in agriculture, numerous policies worldwide have set goals on decreasing the intensity of pesticide application. In particular, the EU’s Farm to Fork (F2F) Strategy aims to reduce 50% pesticide use by 2030 (European Commission 2020), the US’s Environmental Protection Agency (EPA) has established a reduced risk pesticide program (EPA 2024), and Brazil recently launched a national program (Pronara) to minimize pesticide use and promote sustainable agriculture (Gottems 2025). In this context, many researchers have conducted research projects for identifying efficient pesticide reduction strategies in agriculture, ranging from decision support tools for pesticide application to agroecological practices based on crop diversification (e.g., Fig. 1). The strategies proposed are very diverse. For example, Hansen et al. (2000) assessed the effects of band application of herbicides with interrow cultivation for maize crop in the USA, and found that crop yield remained similar despite higher weed abundances. By using mating disruption and reduced pesticide application frequencies for an apple orchard in France, Dib et al. (2016) demonstrated positive influence on the abundance of natural enemies in the fields. Yan et al. (2021) designed a nanoinsecticide for more efficient control of the Myzus persicae (green peach aphid) in oilseed rape in China. Due to the high number of pesticide reduction strategies tested and the diversity of the evaluation methods implemented, the knowledge regarding the performances of pesticide reduction strategies is scattered. A few literature reviews regarding pesticide reduction were performed, but they generally focus on very specific aspects. For example, Patil et al. (2023) conducted a review on automatic variable-rate spraying system in orchards, and Lázaro et al. (2021) investigated decision support systems for fungicide application. In this paper, we aim to conduct a systematic review of the studies evaluating pesticide reduction strategies. We identify the different types of strategies currently proposed in the literature and describe the methods implemented to assess their agronomical, environmental, and economic performances. We compare the probability of positive effects of pesticide reduction strategies on economic output, pest control efficacy, pesticide use, and side effects (non-target species, human health, and the environment). We also evaluate which factors may influence the performance of pesticide reduction strategies. Finally, we identify key knowledge gaps and provide future perspectives. 2 Methodology We collected and selected the relevant secondary literatures (systematic reviews, meta-analyses, and narrative reviews) using a transparent and systematic approach, extracted data from the primary literatures included in the selected secondary literatures, and analyzed the extracted data using statistical and text mining techniques. These steps are described in details below. 2.1 Systematic literature search of secondary literatures This literature review addresses several questions: What is the evidence underlying pesticide reduction strategies in crop production systems? What are the methods used by researchers to evaluate pesticide use reduction strategies? What impacts do pesticide reduction strategies have on economic output, crop productivity, non-target species, human health, and the environment? Which factors may influence the performance of pesticide reduction strategies? Table 1 presents the PICO table used to frame our review according to standard items representing Population, Intervention, Comparator, and Outcome (Richardson et al. 1995). Supplementary Table S1 shows the structure and combination of the search string for the literature search. In order to assess the quality of the search string, six test papers (identified based on the knowledge of the authors as key papers that should be included in the results of the systematic literature search) were used to check if the developed search string could identify all these papers (Supplementary Table S1). Table 1 The PICO table for the systematic literature review. PICO Component Inclusion criteria Population Secondary literature Correspond to literature reviews, meta-analyses, systematic reviews, or evidence maps. Population Crop production systems Focus on crop production (arable, horticulture), not on animal husbandry, dairy farm, or aquaculture. Intervention Pesticide reduction strategies Assess one/several types of pesticide reduction strategies. Comparator Conventional pesticide routines Include at least one conventional pesticide use strategy (defined according to local practices). Outcome Impacts of pesticide reduction strategies on indicators Consider impacts of pesticide reduction strategies on one/several indicators related to pesticide use intensity, crop productivity, the environment, human health, social welfare, or farmers’ income. Separate searches were conducted in June 2024 using two literature databases: ISI Web of Science and Scopus. We searched review papers published in 2023 and 2024 in order to focus on the most recent research syntheses, but did not use any publication date restriction for the primary literatures. The languages used to write the papers include English, French, and Chinese. 2.2 Screening and selection of secondary literatures 787 records were initially identified and 629 after removing duplicates (Fig. 2). Initial screening of title and abstract was conducted via the Rayyan application (Ouzzani et al. 2016), and 29 papers were proceeded to the full text screening. The exclusion criteria were: 1) the paper was outside the scope of this study; 2) it was not a secondary literature; 3) it did not measure the effects of pesticide reduction strategies compared with conventional routines; 4) full text was not available. Finally, ten review papers were selected, including five systematic reviews, two meta-analyses, and three narrative reviews (Supplementary Table S2). 2.3 Selection criteria and data extraction of primary literatures We then retrieved the primary literatures included in the selected review papers from the reference lists provided by the authors. The selection criteria applied to the primary literatures were: 1) the main aim of the paper is to evaluate pesticide reduction strategies in crop production systems involving arable crops or horticulture; 2) The paper includes a conventional pesticide application strategy (i.e., typical traditional practice of the studied region) as control, 3) the paper analyzes the effects of one or several pesticide reduction strategies (compared with conventional pesticide application) on one or several outcomes (crop production, profit, pest control, pesticide input, human health, non-target species, and the environment). In the following, a “study” refers to an experiment, survey, or model-based analysis that compares the performance of a pesticide reduction strategy (single or composite) to that of a conventional pesticide practice. One paper may include one or several studies corresponding to different study locations, crop species, or pests. From the selected primary literatures (Supplementary Table S3), we extracted the paper abstract, study locations, study duration (years), crop types, pesticide categories, pesticide reduction strategies, the methods implemented to assess these strategies (e.g., field experiment, model, interview), and impact indicators. The impact indicators, reflecting the tested effects of pesticide reduction strategies (compared with conventional pesticide routines), were allocated to four categories: economic output, pest control efficacy, pesticide related input, and side effect (Supplementary Table S4). Based on the results of the selected primary literatures, we rated the effect of each pesticide reduction strategy on each impact indicator as follows: significantly negative, negative but no statistical tests, similar, significantly positive, positive but no statistical tests, mixed (data showing either positive or negative, depending on the situation considered). “Significantly positive/negative” means that comparing with conventional pesticide routines, pesticide reduction strategies have significantly positive/negative effects on the impact indicator concerned. A positive effect refers to an effect that is beneficial to the farmers, society, biodiversity, or the environment. For example, if a study showed a “significantly positive” effect on weed density, it means that the pesticide reduction strategies examined were able to reduce weed density (Supplementary Table S4). If the authors stated positive/negative effects in a study without using statistical tests, then such effects were rated as “positive/negative but no statistical tests”. 2.4 Topic modelling Topic modelling is a text mining technique that discovers topics/themes among a collection of documents. In this paper, topic modelling was conducted for the collected abstracts from the literature review. This analysis was performed with R 4.4.2 (R Core Team 2024), with the packages “tidytext”, “topicmodels”, and “ggplot2” (Silge and Robinson 2016; Wickham 2016; Grün and Hornik 2024). First, the individual words for each abstract were extracted. Then, we excluded the common words (i.e., stop words) that carry little meaningful information (e.g., “the”). “Corn” was changed to “maize”. Plurals were changed to singles, and words including numbers/special letters were excluded as well. From the top 100 most frequent words, the generic words that could not be used to distinguish the abstracts were also removed (e.g., “crop”). Then, Latent Dirichlet allocation (LDA) was used to identify two topics summarizing most of the information included in the abstracts of the selected primary literatures. LDA is an unsupervised, probabilistic modelling method that extracts topics from a collection of documents by calculating the per-topic-per-word probabilities (beta) (Silge and Robinson 2017; Asmussen and Møller 2019). Beta value reflects the probability of a word being generated from a topic; thus, a higher beta value means that the corresponding word is more representative of that topic (Silge and Robinson 2017). For each abstract, we also produced the per-document-per-topic probabilities (gamma). Gamma value estimates the proportion of words from a document that are generated from a topic. Thus, each abstract could be represented by the gamma values associated to the different topics, which reflect the level of connection of the abstract to each topic (the sum of the gammas over topics is equal to one for each abstract) (Supplementary Table S3). 2.5 Generalized linear mixed-effects model We used several generalized linear mixed-effects models (GLMMs) with a binomial distribution and logit link (“lme4” package) to identify the drivers of the chance of success (positive effect) of the different pesticide reduction strategies considered in the literature (Bates et al. 2015). For each impact indicator, a binary response variable was defined to describe the effects of the different pesticide reduction strategies, and was set equal to 1 if the effect was “positive” and to 0 otherwise. “Positive” includes “significantly positive” and “positive but no statistical tests” (Supplementary Table S4), but we conducted a sensitivity analysis by removing the cases without statistical tests and repeated the analysis in order to check that the main conclusions were unchanged. The following factors were considered as fixed effects: categories of impact (Supplementary Table S4), number of study years, study location, topic derived from the topic modelling (only the gamma value of topic 2 was used because topic 1 + topic 2 = 1; Supplementary Table S3), crop (cereals vs. fruits), pesticide (insecticides, herbicides, and fungicides), number of pesticide reduction strategies used in a study (organic system was excluded), cropping system (here we compared organic system with the other pesticide reduction strategies, with the other strategies termed as “low-input system”; Table 2). When comparing the cropping system, the data from the impact category “pesticide related input” (Supplementary Table S4) were removed, as this factor was not relevant for organic system. A separate GLMM was used for testing each one of these factors, and the effects were considered statistically significant when p < 0.05. Paper ID was treated as random effect. 2.6 Fisher’s exact test We compared the performance of organic system and low-input system on each impact category (i.e., economic output, pest control efficacy, and side effect) (Supplementary Table S4). Because of the small sample size of the organic system, a 2×2 Fisher’s exact test was used. The effect categories were regrouped as “positive” and “other effects”. “Positive” includes “significantly positive” and “positive but no statistical tests” (Supplementary Table S4), and “other effects” including the effects other than “positive”. Sensitivity analysis was also conducted by removing the cases without statistical tests. 3 Evidence synthesis In total, 97 relevant primary literatures were collected from the ten secondary literatures from the systematic search (Supplementary Table S2, S3). These primary literatures were published in the international peer-reviewed journals from 1992 to 2024, and 69% of papers were published after 2010. The 135 extracted studies of the pesticide use reduction strategies were mostly performed during one to three years, in Europe, North America, and Asia (Fig. 3). In terms of crops, fruits and cereals were mostly assessed in the collected literature (Fig. 4a). Herbicide was the top researched pesticide group among the collected papers (Fig. 4b). 3.1 Assessment methods The most frequent method used to assess pesticide use reduction strategies was field experiment (83%) (Fig. 5). This is an expected result, as field experiments (compared with lab experiments, 5%) are often used to evaluate and compare different crop management practices in field conditions (de Janvry and Sadoulet 2020). Interviews (5%), especially targeting farmers, were used by scientists to involve farmers (i.e., the decision-makers regarding pest management) for defining and evaluating alternative farming practices. This is an important method to understand farmers’ concerns and practical constraints (Zhang et al. 2018; Andert et al. 2021). Modelling techniques were used in 5% of the studies, in particular to analyze the socio-economic aspects of pesticide reduction strategies (e.g., Vorotnikova et al. 2014). In addition, we found three human health studies (2%) on dietary exposure to pesticides. The approach used for these studies relied on detecting pesticide traces in urine (e.g., Hyland et al. 2019). 3.2 Types of pesticide reduction strategies We identified 16 individual pesticide reduction strategy types from the collected primary literatures, which were allocated to eight groups (Table 2). Note that a study reported in a paper may contain multiple pesticide reduction strategies. The most frequent pesticide reduction strategy reported in the literature was site-specific pesticide application (Table 2). It consists of the adjustment of pesticide applications (in terms of volume and/or frequency) according to observed pest occurrence situation in the fields (Gerhards et al. 2022). Many previously published literatures have shown that this method could effectively reduce the pesticide amount (Manandhar et al. 2020; Zhang et al. 2023). Physical/mechanical control (second place) and advanced machinery (third place) were often used together with site-specific pesticide application (e.g., Wiltshire et al. 2003). Advanced machinery is considered as a useful and emerging tool to facilitate the application of site-specific pesticide application and/or physical/mechanical control. For example, Tewari et al. 2014 used an advanced herbicide applicator that consisted of a camera and microcontroller for variable rate spraying in maize and peanuts in India, which could significantly reduce pesticide amount compared with conventional routines. Table 2 Categories of pesticide reduction strategies, the number of studies reported in the literature, and examples. Strategy groups Pesticide reduction strategies Number of studies Examples Monitoring crop monitoring 14 Leaf area index, canopy development pest monitoring 23 UAV (unmanned aerial vehicle), GPS, DSS (decision support system), threshold-based application Diversified cropping system undersown plants 2 Sow clover under fruit trees diversified crop rotation 1 Grow a rich variety of different crops in a planned sequence crop varietal diversity 1 Grow multiple cultivars of one cash crop in the field resistant crop cultivars 1 Grow the crop cultivars that are resistant to pests semi-natural habitat management 2 Hedgerows Physical/mechanical control physical/mechanical 45 Hand weeding, mulching, fruit bagging, harrow, hoe, pruning, mowing Biological control biological control 4 Natural enemies Behavioral control behavior control 11 Mating disruption, light traps, yellow sticky traps, pheromone traps, push-pull Lower pesticide intensities site-specific pesticide application 60 Variable-rate spraying, patch spraying, grid spraying, band application reduced-risk pesticides 20 Biopesticides fixed reduction of pesticide use intensities 12 Fixed reduction pesticide application rates and application frequencies Advanced technology advanced machinery 25 Robot, UAV sprayer, vision-guided hoe nanotechnology 1 Particles of dimension sized from 1 to 100 nanometers, i.e., nanopesticides Organic organic 17 Organic farming system 3.3 Impact indicators From the 135 collected studies, 51 different types of impact indicators used to inform the performance of pesticide reduction strategies were identified and grouped into four categories: economic output, pest control efficacy, pesticide related input, and side effect (Supplementary Table S4). Based on GLMM, pesticide reduction strategies showed significantly higher chances (i.e., higher probability of positive effects) to reduce pesticide input (predicted probability of positive effects = 0.85) and side effect (human health, non-target species, and the environment) (0.64) than to improve economic output (0.08) and pest control efficacy (0.12) (Fig. 6) (p < 0.01). The result was similar when removing the results not confirmed by statistical tests. It is logical to find that pesticide reduction strategies have relatively high capability to reduce pesticide input, as these strategies were defined for this purpose specifically. Interestingly, we found that these strategies were also often able to reduce side effects from pesticide contamination (Maeder et al. 2002; Dib et al. 2016). On the other hand, uncertainties remain whether these strategies could offer sufficient pest control efficacy and economic profitability (e.g., Niazmand et al. 2008). In GLMMs, the response variable can take two levels, “positive effect” (denoted as 1) and “other effects” (0, including “negative”, “mixed” and “similar effect”). Alternatively, if we combine “similar effect” and “positive effect” in the same “positive” category, then 71% and 58% of the effects on economic output and pest control efficacy become “positive”, respectively (Supplementary Table S4). However, when we group the impact categories “economic output” and “pest control efficacy” together as Group A, and the other two impact categories together as group B, then 15% and 20% of the number of effects found in group A are “negative” and “mixed”, respectively (Supplementary Table S4). In comparison, only 2% and 2% of the effects found in group B are “negative” and “mixed”, respectively. The small number of studies showing profitable gains associated with pesticide reduction strategies may be an obstacle to the adoption of these strategies by farmers (Alwang et al. 2019; Deguine et al. 2021). 3.4 Main topics addressed from the review We used topic modelling to help summarize the textual information of the selected papers (Asmussen and Møller 2019; Ali and Kannan 2022). This approach allowed us to discover two main topics addressed in our collection of documents (Fig. 7). These two topics are related to pesticide management issues concerning insect and disease control in fruit orchards (in topic 2) and to weed control in cereals (in topic 1). Previous research also showed that insect pest and disease are a source of concerns in fruit production systems, and weed is an important source of concerns in cereals (Nazarko et al. 2005; Oerke 2006; Serrie et al. 2024). The term “IPM” (integrated pest management) is associated with topic 2, because IPM is often applied in orchards (Ryalls et al. 2024). “Band” in topic 1 could be largely connected to the collected literature on band herbicide application in row crops (Ozaslan et al. 2024). If we assign each collected primary literatures to its dominant topic, then 54 papers are mainly related to topic 1, and 43 to topic 2, showing that our dataset is well balanced (Supplementary Table S3). 4 Factors influencing the impacts of pesticide reduction strategies Among the factors we examined, the number of study years and locations did not influence the probabilities of positive effects of pesticide reduction strategies on the impact indicators (Supplementary Table S4). In terms of topics, studies that relate more with topic 2 had higher probabilities to report positive effects from pesticide reduction strategies on impact indicators (Fig. 8) (p < 0.04 when including/excluding no statistical tests). This finding is in line with crop categories, where we found that fruits may have higher chance of positive effects from pesticide reduction strategies than cereals (0.55 vs. 0.11) (Fig. 6) (p < 0.01). This finding is also in line with pesticide categories, where we found that reduction of fungicides (0.47) and insecticides (0.57) showed higher chance of positive effects than reduction of herbicides (0.10) (Fig. 6). One possible explanation is that pesticide treatments are more frequent in fruit orchards than in cereals (Hossard et al. 2017; Zaller et al. 2023; Serrie et al. 2024). Thus, when pesticide reduction strategies are applied, the magnitude of the positive effect is larger for fruit orchards than cereals. Interestingly, we also found that the probability of positive effect was higher with combined pesticide reduction strategies than single strategies (Fig. 6), although this result became weaker when excluding the effects without statistical tests (p = 0.01 when including no statistical tests, and p = 0.05 when excluding no statistical tests). This indicates that combining several control strategies may enhance the effectiveness of pesticide reduction. Previous research also showed that combining diverse strategies is useful to ensure efficient pest management (Riemens et al. 2022; Masson et al. 2024). On the other hand, mixed effects were found in the meta-analysis conducted by Ryalls et al. (2024), who scored the level of IPM adoption based on the number of aspects of IPM used in each study (i.e., cultural control, mechanical/physical control, biological control, and reduced/alternative pesticides). They found that a higher level of IPM had positive effects on beneficial invertebrates, negative impacts on decrease pressure, and similar effects on herbivore pressure. The result indicated that organic system had overall more positive effects than low-input system (0.63 vs. 0.11) across all the impact categories (excluding pesticide input as this aspect is not relevant for organic system) (Fig. 6). This result can be seen as consistent with the fact that organic system often combines more diverse pesticide reduction strategies than low-input system (Nazarko et al. 2005). Fisher’s exact test showed that organic system performed similarly to low-input system regarding the economic output aspect (p > 0.07) but performed better for side effects (p < 0.03). This is consistent with previous research showing that organic farming could support biodiversity better than low-input system, probably due to the fact that organic system does not use synthetic pesticides (Katayama et al. 2019). In terms of pest control aspect, our analysis suggested that organic system performed better than low-input system (p = 0.04, odds ratio = 8). However, due to the large confidence interval from the small sample size of the organic system (odds ratio ranging from 0.8 to 100), this result should be handled with caution. 5 Conclusions For the first time, we conducted a global systematic literature review on the evaluation and performances of the available pesticide reduction strategies in agriculture. From the 16 collected individual pesticide reduction strategies, the most investigated strategies were site-specific pesticide application, physical/mechanical control, and advanced machinery. Most of the strategies were tested by field experiments, although other assessment methods were also considered, in particular lab experiments, model-based analysis and interview. We found that pesticide reduction strategies performed better regarding the reduction of pesticide input and side effects (non-target species, human health, and the environment) than regarding the improvement of economic output and pest control efficacy. Positive effects were more frequently reported in studies focusing on insect/disease management in fruit orchards and when several strategies were combined together. Organic system produces more positive results than low-input system, especially regarding the side effect aspect. Future research should focus on finding strategies (especially integrated management strategies) that are able to improve pest control efficacy and economic profitability. Acknowledgements We sincerely thank Joana Ferreira for help on the systematic literature search protocol and the FORTUNA partners for their valuable suggestions. Declarations Funding: This project has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement No 101137089 (FORTUNA). This project also received funding from RMT SDMAA (French Ministry of Agriculture) and the CLAND Institute (French National Research Agency). Conflicts of interest/Competing interests: The authors declare no competing interests. Ethics approval: Not applicable Consent to participate: Not applicable Consent for publication: Not applicable Availability of data and material: The datasets generated during and/or analyzed during the current study are available in the figshare repository (DOI: 10.6084/m9.figshare.30196753 ). Code availability: The code generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Authors' contributions: H.Z. and D.M. contributed to the study conception and design. Literature search and data analysis were performed by H.Z. 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Pest Manag Sci 77:1954–1962. https://doi.org/ https://doi.org/10.1002/ps.6223 Zaller JG, Oswald A, Wildenberg M, et al (2023) Potential to reduce pesticides in intensive apple production through management practices could be challenged by climatic extremes. Sci Total Environ 872:162237. https://doi.org/ https://doi.org/10.1016/j.scitotenv.2023.162237 Zhang H, Potts SG, Breeze T, Bailey A (2018) European farmers’ incentives to promote natural pest control service in arable fields. Land use policy 78:682–690. https://doi.org/ https://doi.org/10.1016/j.landusepol.2018.07.017 Zhang J, Yin H, Zhou L, et al (2023) Variable rate air-assisted spray based on real-time disease spot identification. Pest Manag Sci 79:402–414. https://doi.org/ https://doi.org/10.1002/ps.7209 Zhou W, Li M, Achal V (2025) A comprehensive review on environmental and human health impacts of chemical pesticide usage. Emerg Contam 11:100410. https://doi.org/ https://doi.org/10.1016/j.emcon.2024.100410 Additional Declarations The authors declare no competing interests. Supplementary Files PreprintSupplementaryfile.docx Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Agronomy for Sustainable Development → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":357015,"visible":true,"origin":"","legend":"\u003cp\u003eIntercropping is often considered as an agroecological practice able to reduce pesticide use. This picture shows a maize-soybean intercropping practice in China (Gansu province).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/11a7ca89b3cdf9139540e28d.jpeg"},{"id":92661510,"identity":"25bef6f2-92ec-45d2-9889-3d096bcdce8a","added_by":"auto","created_at":"2025-10-02 15:01:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77816,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the screening process of the systematic literature review.\u003c/p\u003e","description":"","filename":"Screenshot20251002at10.57.33AM.png","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/4dc0d4b2897fb40d4c227dbe.png"},{"id":92661513,"identity":"9460a824-4014-4e2b-9f89-e32ec7cfb6cb","added_by":"auto","created_at":"2025-10-02 15:01:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144770,"visible":true,"origin":"","legend":"\u003cp\u003eCounts (a) and world map (b) of the 135 study locations (from 97 primary literatures) collected from the literature review. Fig. 3b was built with the R package “rnaturalearth” and “rnaturalearthdata” (Massicotte and South 2023; South et al. 2024).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/25f2aaad704ffb8b2909cd8c.png"},{"id":92661509,"identity":"63290f24-76a8-4548-9da7-3cbe6e814f64","added_by":"auto","created_at":"2025-10-02 15:01:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93983,"visible":true,"origin":"","legend":"\u003cp\u003eCrop categories (a) and pesticide categories (b) covered by the 135 studies (from 97 primary literatures) collected from the literature review.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/bb58a102b4b6bbc78e280091.png"},{"id":92662201,"identity":"23fe8197-f1eb-4626-8878-0a4b99561b98","added_by":"auto","created_at":"2025-10-02 15:09:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6839,"visible":true,"origin":"","legend":"\u003cp\u003eMethods used to assess pesticide use reduction strategies in the 135 studies (97 primary literatures) collected from the literature review. Some studies relied on several methods.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/d2c50eed8e26aaf0ccf524a6.png"},{"id":92662203,"identity":"4630d824-618c-4685-adeb-717f2e431a8f","added_by":"auto","created_at":"2025-10-02 15:09:26","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":338976,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probabilities of positive effects of pesticide reduction strategies among the four impact categories. Results also show the influences of crop categories, pesticide types, number of strategies, and cropping systems on the predicted probabilities of positive effects of pesticide reduction strategies. Probabilities were estimated using generalized linear mixed-effects models. The error bars represent the 95% confidence intervals. Different letters indicate the significant differences (p=0.05). In this figure, “positive effect” includes “positive but no statistical tests” and “significantly positive” (Supplementary Table S4).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/983db66f165da852b06cd693.jpeg"},{"id":92661521,"identity":"81784040-b6f5-4a4e-9b6c-6e8d55446dff","added_by":"auto","created_at":"2025-10-02 15:01:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":21971,"visible":true,"origin":"","legend":"\u003cp\u003eTwo topics derived by topic modelling from the collected abstracts of the 97 primary literatures of the literature review. The terms listed are the most important within each topic. Beta value reflects the probability of a word being generated from a topic.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/fbd8a038e2dde6bd078f4deb.png"},{"id":92662206,"identity":"67feb4b0-45d7-48a1-ba5e-d5e975fa119f","added_by":"auto","created_at":"2025-10-02 15:09:26","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":26825,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of topic 2 (Fig. 7) on the predicted probabilities of positive effects of pesticide reduction strategies based on the generalized linear mixed-effects model. The blue shades represent the 95% confidence intervals. In this figure, “positive effect” includes “positive but no statistical tests” and “significantly positive” (Supplementary Table S4). The figure was built with the R package “effects” (Fox and Hong 2009).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/8611ba47a8e0a2784add6ce9.png"},{"id":103442605,"identity":"563414c1-450e-4575-ae55-5db704dbf4b8","added_by":"auto","created_at":"2026-02-25 17:40:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1675494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/b751c51f-3912-4a3f-8198-b81d9129b9dc.pdf"},{"id":92662365,"identity":"2cc72e9c-8d6b-4291-a1bc-897f128194e1","added_by":"auto","created_at":"2025-10-02 15:17:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":77701,"visible":true,"origin":"","legend":"","description":"","filename":"PreprintSupplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722542/v1/0bf786cceec26ce3a6ba1c2e.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEvaluation of pesticide reduction strategies in cropping systems. A review\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSixty years after the iconic book \u003cem\u003eSilent Spring\u003c/em\u003e, synthetic pesticides are still at the heart of major environmental burdens (Carson 1962). Largely due to the intensification of pesticide use, the global pesticide consumption grew 20% by volume over the last decade (Shattuck et al. 2023). Pesticides continue to be detrimental toward biodiversity (e.g., insects, birds, wild plants) (Serr\u0026atilde;o et al. 2022; Wan et al. 2025; Jim\u0026eacute;nez-Pe\u0026ntilde;uela et al. 2025), human health (Shekhar et al. 2024; Zhou et al. 2025), and the environment (e.g., water, soil, air) (Tudi et al. 2021; Elumalai et al. 2025). The indiscriminate use of pesticides also causes pest resistance, target pest resurgence, and second pest outbreaks, resulting in risks of insufficient pest management and financial losses for farmers (Hawkins et al. 2019; S\u0026aacute;nchez-Bayo 2021).\u003c/p\u003e\n\u003cp\u003eTo tackle the unsustainable use of pesticides in agriculture, numerous policies worldwide have set goals on decreasing the intensity of pesticide application. In particular, the EU\u0026rsquo;s Farm to Fork (F2F) Strategy aims to reduce 50% pesticide use by 2030 (European Commission 2020), the US\u0026rsquo;s Environmental Protection Agency (EPA) has established a reduced risk pesticide program (EPA 2024), and Brazil recently launched a national program (Pronara) to minimize pesticide use and promote sustainable agriculture (Gottems 2025).\u003c/p\u003e\n\u003cp\u003eIn this context, many researchers have conducted research projects for identifying efficient pesticide reduction strategies in agriculture, ranging from decision support tools for pesticide application to agroecological practices based on crop diversification (e.g., Fig. 1). The strategies proposed are very diverse. For example, Hansen et al. (2000) assessed the effects of band application of herbicides with interrow cultivation for maize crop in the USA, and found that crop yield remained similar despite higher weed abundances. By using mating disruption and reduced pesticide application frequencies for an apple orchard in France, Dib et al. (2016) demonstrated positive influence on the abundance of natural enemies in the fields. Yan et al. (2021) designed a nanoinsecticide for more efficient control of the \u003cem\u003eMyzus persicae\u0026nbsp;\u003c/em\u003e(green peach aphid) in oilseed rape in China.\u003c/p\u003e\u003cp\u003eDue to the high number of pesticide reduction strategies tested and the diversity of the evaluation methods implemented, the knowledge regarding the performances of pesticide reduction strategies is scattered. A few literature reviews regarding pesticide reduction were performed, but they generally focus on very specific aspects. For example, Patil et al. (2023) conducted a review on automatic variable-rate spraying system in orchards, and Lázaro et al. (2021) investigated decision support systems for fungicide application.\u003c/p\u003e\u003cp\u003eIn this paper, we aim to conduct a systematic review of the studies evaluating pesticide reduction strategies. We identify the different types of strategies currently proposed in the literature and describe the methods implemented to assess their agronomical, environmental, and economic performances. We compare the probability of positive effects of pesticide reduction strategies on economic output, pest control efficacy, pesticide use, and side effects (non-target species, human health, and the environment). We also evaluate which factors may influence the performance of pesticide reduction strategies. Finally, we identify key knowledge gaps and provide future perspectives.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cp\u003eWe collected and selected the relevant secondary literatures (systematic reviews, meta-analyses, and narrative reviews) using a transparent and systematic approach, extracted data from the primary literatures included in the selected secondary literatures, and analyzed the extracted data using statistical and text mining techniques. These steps are described in details below.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Systematic literature search of secondary literatures\u003c/h2\u003e\u003cp\u003eThis literature review addresses several questions: What is the evidence underlying pesticide reduction strategies in crop production systems? What are the methods used by researchers to evaluate pesticide use reduction strategies? What impacts do pesticide reduction strategies have on economic output, crop productivity, non-target species, human health, and the environment? Which factors may influence the performance of pesticide reduction strategies? Table\u0026nbsp;1 presents the PICO table used to frame our review according to standard items representing Population, Intervention, Comparator, and Outcome (Richardson et al. 1995). Supplementary Table S1 shows the structure and combination of the search string for the literature search. In order to assess the quality of the search string, six test papers (identified based on the knowledge of the authors as key papers that should be included in the results of the systematic literature search) were used to check if the developed search string could identify all these papers (Supplementary Table S1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe PICO table for the systematic literature review.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePICO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInclusion criteria\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary literature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCorrespond to literature reviews, meta-analyses, systematic reviews, or evidence maps.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrop production systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFocus on crop production (arable, horticulture), not on animal husbandry, dairy farm, or aquaculture.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePesticide reduction strategies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAssess one/several types of pesticide reduction strategies.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConventional pesticide routines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInclude at least one conventional pesticide use strategy (defined according to local practices).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImpacts of pesticide reduction strategies on indicators\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConsider impacts of pesticide reduction strategies on one/several indicators related to pesticide use intensity, crop productivity, the environment, human health, social welfare, or farmers\u0026rsquo; income.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSeparate searches were conducted in June 2024 using two literature databases: ISI Web of Science and Scopus. We searched review papers published in 2023 and 2024 in order to focus on the most recent research syntheses, but did not use any publication date restriction for the primary literatures. The languages used to write the papers include English, French, and Chinese.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Screening and selection of secondary literatures\u003c/h2\u003e\u003cp\u003e787 records were initially identified and 629 after removing duplicates (Fig.\u0026nbsp;2). Initial screening of title and abstract was conducted via the Rayyan application (Ouzzani et al. 2016), and 29 papers were proceeded to the full text screening. The exclusion criteria were: 1) the paper was outside the scope of this study; 2) it was not a secondary literature; 3) it did not measure the effects of pesticide reduction strategies compared with conventional routines; 4) full text was not available. Finally, ten review papers were selected, including five systematic reviews, two meta-analyses, and three narrative reviews (Supplementary Table S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Selection criteria and data extraction of primary literatures\u003c/h2\u003e\u003cp\u003eWe then retrieved the primary literatures included in the selected review papers from the reference lists provided by the authors. The selection criteria applied to the primary literatures were: 1) the main aim of the paper is to evaluate pesticide reduction strategies in crop production systems involving arable crops or horticulture; 2) The paper includes a conventional pesticide application strategy (i.e., typical traditional practice of the studied region) as control, 3) the paper analyzes the effects of one or several pesticide reduction strategies (compared with conventional pesticide application) on one or several outcomes (crop production, profit, pest control, pesticide input, human health, non-target species, and the environment).\u003c/p\u003e\u003cp\u003eIn the following, a \u0026ldquo;study\u0026rdquo; refers to an experiment, survey, or model-based analysis that compares the performance of a pesticide reduction strategy (single or composite) to that of a conventional pesticide practice. One paper may include one or several studies corresponding to different study locations, crop species, or pests. From the selected primary literatures (Supplementary Table S3), we extracted the paper abstract, study locations, study duration (years), crop types, pesticide categories, pesticide reduction strategies, the methods implemented to assess these strategies (e.g., field experiment, model, interview), and impact indicators. The impact indicators, reflecting the tested effects of pesticide reduction strategies (compared with conventional pesticide routines), were allocated to four categories: economic output, pest control efficacy, pesticide related input, and side effect (Supplementary Table S4). Based on the results of the selected primary literatures, we rated the effect of each pesticide reduction strategy on each impact indicator as follows: significantly negative, negative but no statistical tests, similar, significantly positive, positive but no statistical tests, mixed (data showing either positive or negative, depending on the situation considered). \u0026ldquo;Significantly positive/negative\u0026rdquo; means that comparing with conventional pesticide routines, pesticide reduction strategies have significantly positive/negative effects on the impact indicator concerned. A positive effect refers to an effect that is beneficial to the farmers, society, biodiversity, or the environment. For example, if a study showed a \u0026ldquo;significantly positive\u0026rdquo; effect on weed density, it means that the pesticide reduction strategies examined were able to reduce weed density (Supplementary Table S4). If the authors stated positive/negative effects in a study without using statistical tests, then such effects were rated as \u0026ldquo;positive/negative but no statistical tests\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Topic modelling\u003c/h2\u003e\u003cp\u003eTopic modelling is a text mining technique that discovers topics/themes among a collection of documents. In this paper, topic modelling was conducted for the collected abstracts from the literature review. This analysis was performed with R 4.4.2 (R Core Team 2024), with the packages \u0026ldquo;tidytext\u0026rdquo;, \u0026ldquo;topicmodels\u0026rdquo;, and \u0026ldquo;ggplot2\u0026rdquo; (Silge and Robinson 2016; Wickham 2016; Gr\u0026uuml;n and Hornik 2024). First, the individual words for each abstract were extracted. Then, we excluded the common words (i.e., stop words) that carry little meaningful information (e.g., \u0026ldquo;the\u0026rdquo;). \u0026ldquo;Corn\u0026rdquo; was changed to \u0026ldquo;maize\u0026rdquo;. Plurals were changed to singles, and words including numbers/special letters were excluded as well. From the top 100 most frequent words, the generic words that could not be used to distinguish the abstracts were also removed (e.g., \u0026ldquo;crop\u0026rdquo;). Then, Latent Dirichlet allocation (LDA) was used to identify two topics summarizing most of the information included in the abstracts of the selected primary literatures. LDA is an unsupervised, probabilistic modelling method that extracts topics from a collection of documents by calculating the per-topic-per-word probabilities (beta) (Silge and Robinson 2017; Asmussen and M\u0026oslash;ller 2019). Beta value reflects the probability of a word being generated from a topic; thus, a higher beta value means that the corresponding word is more representative of that topic (Silge and Robinson 2017). For each abstract, we also produced the per-document-per-topic probabilities (gamma). Gamma value estimates the proportion of words from a document that are generated from a topic. Thus, each abstract could be represented by the gamma values associated to the different topics, which reflect the level of connection of the abstract to each topic (the sum of the gammas over topics is equal to one for each abstract) (Supplementary Table S3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Generalized linear mixed-effects model\u003c/h2\u003e\u003cp\u003eWe used several generalized linear mixed-effects models (GLMMs) with a binomial distribution and logit link (\u0026ldquo;lme4\u0026rdquo; package) to identify the drivers of the chance of success (positive effect) of the different pesticide reduction strategies considered in the literature (Bates et al. 2015). For each impact indicator, a binary response variable was defined to describe the effects of the different pesticide reduction strategies, and was set equal to 1 if the effect was \u0026ldquo;positive\u0026rdquo; and to 0 otherwise. \u0026ldquo;Positive\u0026rdquo; includes \u0026ldquo;significantly positive\u0026rdquo; and \u0026ldquo;positive but no statistical tests\u0026rdquo; (Supplementary Table S4), but we conducted a sensitivity analysis by removing the cases without statistical tests and repeated the analysis in order to check that the main conclusions were unchanged. The following factors were considered as fixed effects: categories of impact (Supplementary Table S4), number of study years, study location, topic derived from the topic modelling (only the gamma value of topic 2 was used because topic 1\u0026thinsp;+\u0026thinsp;topic 2\u0026thinsp;=\u0026thinsp;1; Supplementary Table S3), crop (cereals vs. fruits), pesticide (insecticides, herbicides, and fungicides), number of pesticide reduction strategies used in a study (organic system was excluded), cropping system (here we compared organic system with the other pesticide reduction strategies, with the other strategies termed as \u0026ldquo;low-input system\u0026rdquo;; Table\u0026nbsp;2). When comparing the cropping system, the data from the impact category \u0026ldquo;pesticide related input\u0026rdquo; (Supplementary Table S4) were removed, as this factor was not relevant for organic system. A separate GLMM was used for testing each one of these factors, and the effects were considered statistically significant when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Paper ID was treated as random effect.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Fisher\u0026rsquo;s exact test\u003c/h2\u003e\u003cp\u003eWe compared the performance of organic system and low-input system on each impact category (i.e., economic output, pest control efficacy, and side effect) (Supplementary Table S4). Because of the small sample size of the organic system, a 2\u0026times;2 Fisher\u0026rsquo;s exact test was used. The effect categories were regrouped as \u0026ldquo;positive\u0026rdquo; and \u0026ldquo;other effects\u0026rdquo;. \u0026ldquo;Positive\u0026rdquo; includes \u0026ldquo;significantly positive\u0026rdquo; and \u0026ldquo;positive but no statistical tests\u0026rdquo; (Supplementary Table S4), and \u0026ldquo;other effects\u0026rdquo; including the effects other than \u0026ldquo;positive\u0026rdquo;. Sensitivity analysis was also conducted by removing the cases without statistical tests.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Evidence synthesis","content":"\u003cp\u003eIn total, 97 relevant primary literatures were collected from the ten secondary literatures from the systematic search (Supplementary Table S2, S3). These primary literatures were published in the international peer-reviewed journals from 1992 to 2024, and 69% of papers were published after 2010. The 135 extracted studies of the pesticide use reduction strategies were mostly performed during one to three years, in Europe, North America, and Asia (Fig.\u0026nbsp;3). In terms of crops, fruits and cereals were mostly assessed in the collected literature (Fig.\u0026nbsp;4a). Herbicide was the top researched pesticide group among the collected papers (Fig.\u0026nbsp;4b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Assessment methods\u003c/h2\u003e\u003cp\u003eThe most frequent method used to assess pesticide use reduction strategies was field experiment (83%) (Fig.\u0026nbsp;5). This is an expected result, as field experiments (compared with lab experiments, 5%) are often used to evaluate and compare different crop management practices in field conditions (de Janvry and Sadoulet 2020). Interviews (5%), especially targeting farmers, were used by scientists to involve farmers (i.e., the decision-makers regarding pest management) for defining and evaluating alternative farming practices. This is an important method to understand farmers\u0026rsquo; concerns and practical constraints (Zhang et al. 2018; Andert et al. 2021). Modelling techniques were used in 5% of the studies, in particular to analyze the socio-economic aspects of pesticide reduction strategies (e.g., Vorotnikova et al. 2014). In addition, we found three human health studies (2%) on dietary exposure to pesticides. The approach used for these studies relied on detecting pesticide traces in urine (e.g., Hyland et al. 2019).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Types of pesticide reduction strategies\u003c/h2\u003e\u003cp\u003eWe identified 16 individual pesticide reduction strategy types from the collected primary literatures, which were allocated to eight groups (Table\u0026nbsp;2). Note that a study reported in a paper may contain multiple pesticide reduction strategies. The most frequent pesticide reduction strategy reported in the literature was site-specific pesticide application (Table\u0026nbsp;2). It consists of the adjustment of pesticide applications (in terms of volume and/or frequency) according to observed pest occurrence situation in the fields (Gerhards et al. 2022). Many previously published literatures have shown that this method could effectively reduce the pesticide amount (Manandhar et al. 2020; Zhang et al. 2023). Physical/mechanical control (second place) and advanced machinery (third place) were often used together with site-specific pesticide application (e.g., Wiltshire et al. 2003). Advanced machinery is considered as a useful and emerging tool to facilitate the application of site-specific pesticide application and/or physical/mechanical control. For example, Tewari et al. 2014 used an advanced herbicide applicator that consisted of a camera and microcontroller for variable rate spraying in maize and peanuts in India, which could significantly reduce pesticide amount compared with conventional routines.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCategories of pesticide reduction strategies, the number of studies reported in the literature, and examples.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrategy groups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePesticide reduction strategies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of studies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExamples\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMonitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecrop monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLeaf area index, canopy development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epest monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUAV (unmanned aerial vehicle), GPS, DSS (decision support system), threshold-based application\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDiversified cropping system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eundersown plants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSow clover under fruit trees\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ediversified crop rotation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGrow a rich variety of different crops in a planned sequence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecrop varietal diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGrow multiple cultivars of one cash crop in the field\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eresistant crop cultivars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGrow the crop cultivars that are resistant to pests\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esemi-natural habitat management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHedgerows\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical/mechanical control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ephysical/mechanical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHand weeding, mulching, fruit bagging, harrow, hoe, pruning, mowing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiological control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebiological control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNatural enemies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBehavioral control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebehavior control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMating disruption, light traps, yellow sticky traps, pheromone traps, push-pull\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLower pesticide intensities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esite-specific pesticide application\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariable-rate spraying, patch spraying, grid spraying, band application\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ereduced-risk pesticides\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiopesticides\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efixed reduction of pesticide use intensities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFixed reduction pesticide application rates and application frequencies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAdvanced technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eadvanced machinery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRobot, UAV sprayer, vision-guided hoe\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enanotechnology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eParticles of dimension sized from 1 to 100 nanometers, i.e., nanopesticides\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eorganic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOrganic farming system\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Impact indicators\u003c/h2\u003e\u003cp\u003eFrom the 135 collected studies, 51 different types of impact indicators used to inform the performance of pesticide reduction strategies were identified and grouped into four categories: economic output, pest control efficacy, pesticide related input, and side effect (Supplementary Table S4). Based on GLMM, pesticide reduction strategies showed significantly higher chances (i.e., higher probability of positive effects) to reduce pesticide input (predicted probability of positive effects\u0026thinsp;=\u0026thinsp;0.85) and side effect (human health, non-target species, and the environment) (0.64) than to improve economic output (0.08) and pest control efficacy (0.12) (Fig.\u0026nbsp;6) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The result was similar when removing the results not confirmed by statistical tests.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIt is logical to find that pesticide reduction strategies have relatively high capability to reduce pesticide input, as these strategies were defined for this purpose specifically. Interestingly, we found that these strategies were also often able to reduce side effects from pesticide contamination (Maeder et al. 2002; Dib et al. 2016). On the other hand, uncertainties remain whether these strategies could offer sufficient pest control efficacy and economic profitability (e.g., Niazmand et al. 2008). In GLMMs, the response variable can take two levels, \u0026ldquo;positive effect\u0026rdquo; (denoted as 1) and \u0026ldquo;other effects\u0026rdquo; (0, including \u0026ldquo;negative\u0026rdquo;, \u0026ldquo;mixed\u0026rdquo; and \u0026ldquo;similar effect\u0026rdquo;). Alternatively, if we combine \u0026ldquo;similar effect\u0026rdquo; and \u0026ldquo;positive effect\u0026rdquo; in the same \u0026ldquo;positive\u0026rdquo; category, then 71% and 58% of the effects on economic output and pest control efficacy become \u0026ldquo;positive\u0026rdquo;, respectively (Supplementary Table S4). However, when we group the impact categories \u0026ldquo;economic output\u0026rdquo; and \u0026ldquo;pest control efficacy\u0026rdquo; together as Group A, and the other two impact categories together as group B, then 15% and 20% of the number of effects found in group A are \u0026ldquo;negative\u0026rdquo; and \u0026ldquo;mixed\u0026rdquo;, respectively (Supplementary Table S4). In comparison, only 2% and 2% of the effects found in group B are \u0026ldquo;negative\u0026rdquo; and \u0026ldquo;mixed\u0026rdquo;, respectively. The small number of studies showing profitable gains associated with pesticide reduction strategies may be an obstacle to the adoption of these strategies by farmers (Alwang et al. 2019; Deguine et al. 2021).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Main topics addressed from the review\u003c/h2\u003e\u003cp\u003eWe used topic modelling to help summarize the textual information of the selected papers (Asmussen and M\u0026oslash;ller 2019; Ali and Kannan 2022). This approach allowed us to discover two main topics addressed in our collection of documents (Fig.\u0026nbsp;7). These two topics are related to pesticide management issues concerning insect and disease control in fruit orchards (in topic 2) and to weed control in cereals (in topic 1). Previous research also showed that insect pest and disease are a source of concerns in fruit production systems, and weed is an important source of concerns in cereals (Nazarko et al. 2005; Oerke 2006; Serrie et al. 2024). The term \u0026ldquo;IPM\u0026rdquo; (integrated pest management) is associated with topic 2, because IPM is often applied in orchards (Ryalls et al. 2024). \u0026ldquo;Band\u0026rdquo; in topic 1 could be largely connected to the collected literature on band herbicide application in row crops (Ozaslan et al. 2024). If we assign each collected primary literatures to its dominant topic, then 54 papers are mainly related to topic 1, and 43 to topic 2, showing that our dataset is well balanced (Supplementary Table S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Factors influencing the impacts of pesticide reduction strategies","content":"\u003cp\u003eAmong the factors we examined, the number of study years and locations did not influence the probabilities of positive effects of pesticide reduction strategies on the impact indicators (Supplementary Table S4). In terms of topics, studies that relate more with topic 2 had higher probabilities to report positive effects from pesticide reduction strategies on impact indicators (Fig.\u0026nbsp;8) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.04 when including/excluding no statistical tests). This finding is in line with crop categories, where we found that fruits may have higher chance of positive effects from pesticide reduction strategies than cereals (0.55 vs. 0.11) (Fig.\u0026nbsp;6) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding is also in line with pesticide categories, where we found that reduction of fungicides (0.47) and insecticides (0.57) showed higher chance of positive effects than reduction of herbicides (0.10) (Fig.\u0026nbsp;6). One possible explanation is that pesticide treatments are more frequent in fruit orchards than in cereals (Hossard et al. 2017; Zaller et al. 2023; Serrie et al. 2024). Thus, when pesticide reduction strategies are applied, the magnitude of the positive effect is larger for fruit orchards than cereals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInterestingly, we also found that the probability of positive effect was higher with combined pesticide reduction strategies than single strategies (Fig.\u0026nbsp;6), although this result became weaker when excluding the effects without statistical tests (p\u0026thinsp;=\u0026thinsp;0.01 when including no statistical tests, and p\u0026thinsp;=\u0026thinsp;0.05 when excluding no statistical tests). This indicates that combining several control strategies may enhance the effectiveness of pesticide reduction. Previous research also showed that combining diverse strategies is useful to ensure efficient pest management (Riemens et al. 2022; Masson et al. 2024). On the other hand, mixed effects were found in the meta-analysis conducted by Ryalls et al. (2024), who scored the level of IPM adoption based on the number of aspects of IPM used in each study (i.e., cultural control, mechanical/physical control, biological control, and reduced/alternative pesticides). They found that a higher level of IPM had positive effects on beneficial invertebrates, negative impacts on decrease pressure, and similar effects on herbivore pressure.\u003c/p\u003e\u003cp\u003eThe result indicated that organic system had overall more positive effects than low-input system (0.63 vs. 0.11) across all the impact categories (excluding pesticide input as this aspect is not relevant for organic system) (Fig.\u0026nbsp;6). This result can be seen as consistent with the fact that organic system often combines more diverse pesticide reduction strategies than low-input system (Nazarko et al. 2005). Fisher\u0026rsquo;s exact test showed that organic system performed similarly to low-input system regarding the economic output aspect (p\u0026thinsp;\u0026gt;\u0026thinsp;0.07) but performed better for side effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.03). This is consistent with previous research showing that organic farming could support biodiversity better than low-input system, probably due to the fact that organic system does not use synthetic pesticides (Katayama et al. 2019). In terms of pest control aspect, our analysis suggested that organic system performed better than low-input system (p\u0026thinsp;=\u0026thinsp;0.04, odds ratio\u0026thinsp;=\u0026thinsp;8). However, due to the large confidence interval from the small sample size of the organic system (odds ratio ranging from 0.8 to 100), this result should be handled with caution.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eFor the first time, we conducted a global systematic literature review on the evaluation and performances of the available pesticide reduction strategies in agriculture. From the 16 collected individual pesticide reduction strategies, the most investigated strategies were site-specific pesticide application, physical/mechanical control, and advanced machinery. Most of the strategies were tested by field experiments, although other assessment methods were also considered, in particular lab experiments, model-based analysis and interview. We found that pesticide reduction strategies performed better regarding the reduction of pesticide input and side effects (non-target species, human health, and the environment) than regarding the improvement of economic output and pest control efficacy. Positive effects were more frequently reported in studies focusing on insect/disease management in fruit orchards and when several strategies were combined together. Organic system produces more positive results than low-input system, especially regarding the side effect aspect. Future research should focus on finding strategies (especially integrated management strategies) that are able to improve pest control efficacy and economic profitability.\u003c/p\u003e"},{"header":"Acknowledgements","content":"\u003cp\u003eWe sincerely thank Joana Ferreira for help on the systematic literature search protocol and the FORTUNA partners for their valuable suggestions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis project has received funding from the European Union\u0026rsquo;s Horizon Europe research and innovation program under grant agreement No 101137089 (FORTUNA). This project also received funding from RMT SDMAA (French Ministry of Agriculture) and the CLAND Institute (French National Research Agency).\u003c/p\u003e\u003cp\u003eConflicts of interest/Competing interests: The authors declare no competing interests.\u003c/p\u003e\u003cp\u003eEthics approval: Not applicable\u003c/p\u003e\u003cp\u003eConsent to participate: Not applicable\u003c/p\u003e\u003cp\u003eConsent for publication: Not applicable\u003c/p\u003e\u003cp\u003eAvailability of data and material: The datasets generated during and/or analyzed during the current study are available in the figshare repository (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6084/m9.figshare.30196753\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.30196753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCode availability: The code generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\u003ch2\u003eAuthors' contributions:\u003c/h2\u003e\u003cp\u003eH.Z. and D.M. contributed to the study conception and design. Literature search and data analysis were performed by H.Z. The first draft of the manuscript was written by H.Z. All authors contributed to the review and editing of the manuscript. All authors read and approved the final manuscript. Supervision and funding acquisition were provided by D.M.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eDeclarations\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAli I, Kannan D (2022) Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review. 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Numerous strategies are proposed to reduce the intensity of pesticides; however, the knowledge regarding their performances is scattered. To move forward, it is crucial to identify efficient pesticide reduction strategies. For the first time, based on a systematic review of ten secondary literatures, we reviewed 97 primary literatures reporting 135 studies assessing pesticide reduction strategies. Sixteen types of individual strategy were identified, among which the most investigated strategies were site-specific pesticide application, physical/mechanical control, and advanced machinery. Herbicide was the most studied pesticide group regarding pesticide reduction. The most frequent method considered to assess pesticide reduction strategies was field experiment (83%), followed by lab experiment, interview, and modelling (5% each), and human health study (2%). Fifty-one indicators reflecting the impacts of pesticide reduction strategies were collected and grouped into four impact categories: economic output, pest control efficacy, pesticide related input, and side effect (non-target species, human health, and the environment). Based on generalized linear mixed-effects models, we found that pesticide reduction strategies had higher chance to reduce pesticide input and side effects than to improve economic output and pest control efficacy. Through topic modelling analysis, we were able to allocate the primary literatures to two main topics: topic 1 includes weed management in cereals, and topic 2 is about insect and disease management in fruits. Studies focusing on topic 2 and studies combining several pesticide reduction strategies had higher probabilities to report positive effects from pesticide reduction strategies than topic 1 and single strategies respectively. We also found that organic system produces more positive results than low-input system, especially regarding side effects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Evaluation of pesticide reduction strategies in cropping systems. 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