Pesticide usage in Scotland: soft fruit 2020

Appendix 4 – Survey methodology

Sampling and data collection

Using the June 2020 Agricultural Census⁽¹⁰⁾, two samples were drawn representing soft fruit cultivation in Scotland. The first sample was selected from holdings growing soft fruit crops grown in the open (non-protected crops) and the second from holdings growing soft fruit crops in glasshouses or under walk-in plastic structures (protected crops). Protected and non-protected crops are recorded separately in the Agricultural Census. Separate samples were drawn to ensure non-protected crops were not under-represented in the sample; however, pesticide information was collected for all soft fruit crops grown on all holdings.

The country was divided into 11 land-use regions (Figure 29). Each sample was stratified by these land-use regions and according to holding size. The holding size groups were based on the total area of soft fruit crops grown. The sampling fractions used within both regions and size groups were based on the areas of relevant crops grown rather than number of holdings, so that smaller holdings would not dominate the sample.

The survey covered pesticide applications to soft fruit crops where all or the majority of the growing season was in 2020. As well as recording treatments applied directly to the crop, data was also collected on land preparation treatments prior to sowing or planting the crop.

Following an introductory letter and phone call, data were collected by either a phone interview or by email. Where necessary, information was also collected from agronomists and contractors. In total, information was collected from holdings 64 growing soft fruit crops (Table 25). These holdings represent 36 per cent of the total crop area grown.

Raising factors

National pesticide use was estimated by ratio raising. This is a standard statistical technique for producing estimates from a sample. It is the same methodology used by the other UK survey teams and has been used for all historical datasets produced by the Pesticide Survey Unit, allowing comparability over time. The sample data were multiplied by raising factors (Table 30 and 31). These factors were calculated by comparing the sampled area in each of the two samples to the areas recorded in the Agricultural Census within each region and size group. An adjustment (Table 32 and 33) was made for each crop within each region by applying the raising factors to the sample area of each crop grown and comparing this with the census area. This adjustment modifies the estimate to take into account differences in composition of crops encountered in the sample and those present in the population. A second adjustment is applied if crops which are present in the population are not encountered in all strata of the sample. Due to the distribution of soft fruit crops in Scotland the land use regions were amalgamated into three areas before raising; the North (Highlands & Islands, Caithness & Orkney, Moray and Aberdeen), Angus (the main fruit growing region in Scotland) and the South (East Fife, Lothian, Central Lowlands, Tweed Valley, Southern Uplands and Solway).

Changes from previous years

A change in sample methodology should be noted when comparing the 2020 data with the previous surveys. All data in 2020 was collected using non-visit methods such as phone interview or email due to restrictions imposed by the COVID-19 pandemic. In previous years data were collected by a combination of personal interview during a visit to the holding and/or by phone/email. This change in data collection method may have impacted the number and type of respondents. Every effort was made to achieve a robust sample despite soft fruit growers dealing with EU exit and COVID restrictions which resulted in a reduced number of participants. This additional effort and change in data collection method resulted in a delay to the publication date.

This report presents information about grower adoption of Integrated Pest Management (IPM). IPM data was not collected during the 2018 survey. The data presented represents the second in the series of surveys of IPM measure on soft fruit crops, first collected alongside the 2016 soft fruit crop survey, allowing the adoption of IPM techniques to be monitored.

Data quality assurance

The dataset undergoes several validation processes as follows; (i) checking for any obvious errors upon data receipt (ii) checking and identifying inconsistencies with use and pesticide approval conditions once entered into the database (iii) checking of data held in the database against the raw data. Where inconsistencies are found these are checked against the records and with the grower if necessary. Additional quality assurance is provided by sending reports for review to members of the Working Party on Pesticide Usage Surveys and other agricultural experts. In addition, the Scottish pesticide survey unit is accredited to ISO 9001:2015. All survey related processes are documented in Standard Operating Procedures (SOPs) and our output is audited against these SOPs by internal auditors annually and by external auditors every three years.

Main sources of bias

The use of a random stratified sample is an appropriate survey methodology. A stratified random sample, grouped by farm size and region, is used to select holdings used in this survey. Sampling within size groups is based on area rather than numbers of holdings, so that smaller size groups are not over-represented in the sample. The pesticide survey may be subject to measurement bias as it is reliant on farmers/growers recording data accurately. As this survey is not compulsory it may also subject to non-response bias, as growers on certain farm/holding types may be more likely to respond to the survey than others. Reserve lists of holdings are held for each stratum to allow non-responding holdings to be replaced with similar holdings.

Experience indicates that stratified random sampling, including reserves, coupled with personal interview technique, delivers the highest quality data and minimises non-response bias.