False codling moth Reliable pre-harvest monitoring systems

Context

Pre-harvest infestation monitoring for FCM in citrus orchards is an important component of an effective FCM management strategy. This is particularly so within a systems approach for FCM, as has been implemented for export of citrus to the European Union (Moore et al., 2016; Hattingh et al., 2020). The systems approach requires monitoring of fruit infestation in orchards for at least 12 weeks before harvest (Moore et al., 2016; Hattingh et al., 2020). At the time of this study, the exercise was done by collecting fallen fruit under selected data trees and assessing them for FCM infestation. If infestation exceeded the set threshold, corrective actions or colder shipping conditions were required. This study aimed to determine how accurately and reliably this monitoring system reflected true orchard infestation and to explore development of a better method. Accuracy and reliability of pre-harvest infestation monitoring are critical to ensure the efficacy of the systems approach.

Materials and methods

Trial sites

The study was conducted over two seasons (2021–22) in Navel orange orchards in the Sundays River Valley, Eastern Cape (Figures 1 and 2). In 2021, seven orchards with a history of high FCM infestation were selected. In 2022, seven new orchards with historically low infestation levels were chosen, so that monitoring systems could be tested under different pest pressures.

Trial layout and monitoring

Each orchard was divided into four quadrants, with five marked data trees per quadrant (Figure 3). Fallen fruit were collected twice weekly from these trees for the 12 weeks leading up to harvest (Figure 4). They were then dissected to check for FCM larvae or damage (larva exited fruit). On the same days, sanitation teams collected all fallen fruit from the rest of the orchard. These fruit were assessed in the same way as the quadrant fruit, but separately. Infestation levels were expressed as the number of infested fruit per tree per week.

Data analysis

The study tested whether infestation data from the sets of five data trees accurately represented infestation in the orchard as a whole. The study also determined whether a sample of fruit from the sanitation fruit could alternatively be used to do this. Statistical models were used to compare infestation trends, determine the best sample sizes for accurate estimates, and assess how infestation varied over time. The analyses also examined whether infestation was randomly or unevenly distributed within orchards. All statistical models were performed in R version 4.4.0 (R Core Team, 2024).

Results

Over the two seasons, a total of 134 952 fallen fruit were collected, cut and inspected for FCM (Figure 5).

In the first season there was a strong, positive relationship between infestation recorded from the sets of five data trees and the entire orchard (p < 0.001). This confirmed that the five-data-tree monitoring method provided a reliable estimate of orchard infestation levels. The average infestation per tree per week for data trees was 0.22, whereas for the whole orchard it was 0.13 infested fruit per tree per week. Therefore, data trees slightly overestimated infestation compared to the entire orchard, an advantage for ensuring compliance with infestation thresholds and, hence, market access security. In the second season, overall infestation levels were exceptionally low, between 29 and 333 times below the threshold of 0.1 infested fruit per tree per week. Although the majority of fruit under data trees recorded zero infestation, this was not problematic, as it still provided a reliable indication that orchards remained compliant with the systems approach threshold.

Statistical modelling showed that a 100-fruit sample per orchard per week provided a good estimate for the actual infestation level in the entire orchard. In most cases, infestation in the first 100-fruit sample taken from an orchard was equal to or higher than the orchard as a whole. In cases where the infestation threshold of 0.1 infested fruit per tree per week was exceeded, it was correctly detected in 100% of the first 100-fruit samples taken.

Spatial analysis showed that in both seasons, FCM infestation in orchards was only slightly clustered. Towards the end of the season, during the last four weeks before harvest, FCM infestation became more randomly distributed throughout the orchard.

Across both seasons, infestation levels tended to decline over time, with only a slight increase shortly before harvest. This suggested that monitoring every two weeks instead of weekly should be adequate, including during the last four weeks before harvest.

Further insight

During two seasons, more than 134 000 fallen fruit were collected and inspected for FCM infestation. As this study included all fallen fruit from all study orchards over a 12-week period, the results provided a complete and accurate picture of infestation levels in each orchard and therefore gave us a highly reliable comparison between infestation indicated by the monitoring systems, and actual infestation in the orchards.

The originally used five-data-tree method gave a good estimate of orchard infestation levels. Although data trees recorded slightly higher infestation, possibly due to shelter effects within orchards, the method reliably detected when the threshold of 0.1 infested fruit per tree per week was exceeded. Even if data trees were not optimally positioned, i.e., not positioned where fruit drop in the orchard was observed to be highest, threshold exceedance was detected in over 98% of cases, showing that infestation was relatively evenly distributed and random tree selection would not compromise the efficacy of the system.

However, using data trees can conflict with orchard sanitation practices, which are crucial for reducing FCM populations. The risk is that orchard sanitation teams might remove fruit from underneath data trees, before monitors evaluate infestation of such fruit. To address this, a new system was developed, sampling a portion of the total sanitation fruit instead of using fixed data trees. Consequently, the sanitation team and the monitoring team would now be cooperating, rather than potentially conflicting with each other. Statistical analysis showed that sampling 100 fruit per orchard was sufficiently sensitive to reliably detect when the threshold was exceeded, even in large orchards, and was practical to apply. Random selection of fruit for sampling and inspection is required within the systems approach. But non-adherence, especially in the last four weeks before harvest, would not meaningfully undermine the accuracy of the system.

Infestation levels remained consistent through most of the season, rising slightly only in the last four weeks before harvest. This indicated that fortnightly sampling is sufficient, including two checks in the final month before harvest.

Overall, the study confirmed that the inspection of a randomly selected 100-fruit sample every two weeks for several weeks leading up to harvest is effective and reliable for pre-harvest monitoring of FCM infestation in citrus orchards (Figure 6). This research has been published in an international peer-reviewed scientific journal (Moore et al., 2025).
This study confirmed that two pre-harvest monitoring systems for FCM infestation are highly accurate and reliable, including a 100-fruit sanitation sampling method used in the FCM systems approach. Furthermore, the 100-fruit sanitation sampling method reduces data loss caused by sanitation practices and strengthens SA’s systems approach towards maintaining access to export markets.

Key Findings

• The five-data-tree fallen fruit sampling system was highly reliable for indicating FCM infestation threshold exceedance in the orchard.
• Sampling 100 sanitation fruit per orchard also provided a highly reliable indication of FCM infestation threshold exceedance in the orchard.
• The second method aligns with standard orchard sanitation practices, reducing labour and data loss.
• Fortnightly monitoring provides sufficient data for accurate estimation of FCM infestation status.

References

CGA (Citrus Growers Association). 2024. Key Industry statistics. CGA, Hillcrest, South Africa. Chisoro-Dube, S., and S. Roberts. 2021. Innovations and inclusion in South Africa’s citrus industry. Innovations and Inclusion in Agro-processing. V1: 1 – 53. EU (European Union). 2017. Commission Implementing Directive (EU) 2017/1279 of 14 July 2017 amending Annexes I to V to Council Directive 2000/29/EC on protective measures against the introduction into the community of organisms harmful to plants or plant products and against their spread within the community. Directives originating from the EU, 2017 No. 1279. https://www.legislation.gov.uk/eudr/2017/1279/contents# EU (European Union). 2019. Commission Implementing Regulation (EU) 2019/2072 of 28 November 2019 establishing uniform conditions for the implementation of Regulation (EU) 2016/2031 of the European Parliament and the Council, as regards protective measures against pests of plants, and repealing Commission Regulation (EC) No 690/2008 and amending Commission Implementing Regulation (EU) 2018/2019. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32019R2072 EU (European Union). 2019. Commission Implementing Regulation (EU) 2022/959 f 16 June 2022 amending Annex VII to Implementing Regulation (EU) 2019/2072 as regards requirements for the introduction into the Union of certain fruits of Capsicum (L.), Citrus L., Citrus sinensis Pers., Prunus persica (L.) Batsch and Punica granatum L. https://eur-lex.europa.eu/eli/reg_impl/2022/959/oj FAO (Food and Agriculture Organisation of the United Nations). 2021a. International Standards for Phytosanitary Measures ISPM No. 14: The use of integrated measures in a systems approach for pest risk management. FAO, Rome, Italy. FAO (Food and Agriculture Organisation of the United Nations). 2024. International Standards for Phytosanitary Measures ISPM No. 5: Glossary of phytosanitary terms. FAO, Rome, Italy. Google Earth. (2025). Sundays River Valley, Eastern Cape, South Africa [Satellite imagery]. Google LLC. Imagery ©2025 Maxar Technologies. Retrieved April 20, 2025, from https://earth.google.com/ Hattingh, V., S.D. Moore, W. Kirkman, M. Goddard, S.R. Thackeray, M. Peyper, G. Sharp, P. Cronjé, and K. Pringle. 2020. An Improved Systems Approach as a Phytosanitary Measure for Thaumatotibia leucotreta (Lepidoptera: Tortricidae) in Export Citrus Fruit from South Africa. Journal of Economic Entomology. 113: 700 – 711. Moore, S.D. 2021. Biological control of a phytosanitary pest (Thaumatotibia leucotreta): a case study. International Journal of Environmental Research and Public Health, 18(3), 1198; https://doi.org/10.3390/ijerph18031198 Moore, S.D., W. Kirkman, and V. Hattingh. 2016. Verification of inspection standards and efficacy of a systems approach for Thaumatotibia leucotreta (Lepidoptera: Tortricidae) for export citrus from South Africa. Journal of Economic Entomology. 109: 1564–1570. Moore, S.D., R.U. Ehlers, A. Manrakhan, M. Gilbert, W. Kirkman, J.H. Daneel, J.Y. De Waal, R. Nel, G. Sutton, and A.P. Malan. 2024. Field-scale efficacy of entomopathogenic nematodes to control false codling moth, Thaumatotibia leucotreta (Lepidoptera: Tortricidae), in citrus orchards in South Africa. Crop Protection, 179, p.106610. Moore, S.D., T. Marsberg, M. Peyper, L. Cousins, M. Van Der Merwe, G. Sutton, S. Van Niekerk, and V. Hattingh. 2025. Development and evaluation of preharvest Thaumatotibia leucotreta citrus fruit infestation monitoring for inclusion in a systems approach. Insects. 16 (589): 1–22.

BOX Tamryn Marsberg1, Sean Moore1, 2 Mellissa Peyper1, Luke Cousins1, Marcel van der Merwe2, 3 Guy Sutton2, Sonnica van Niekerk1 and Vaughan Hattingh4

1. Citrus Research International, P.O. Box 5095, Walmer, Gqeberha, 6065 South Africa
2. Centre for Biological Control, Department of Zoology and Entomology, Rhodes University, P.O. Box 94, Makhanda, 6140, South Africa
3. Department of Biochemistry and Microbiology, Rhodes University, P.O. Box 94, Makhanda 6140, South Africa
4. Citrus Research International, Department of Horticultural Sciences, Stellenbosch University, Stellenbosch 7600, SA.