Farmland breeding wader populations have declined drastically in recent decades. Since 1994 in the UK we have lost over half of our breeding Curlews, Lapwings, and Redshanks. These declines are associated with a range of factors, including increased rates of predation, afforestation, and the intensification of field management in farmed landscapes.

The primary tool by which wader conservation is delivered in farmed landscapes is agri-environment schemes (AES), whereby payments incentivise land managers to conduct beneficial management for species or habitats. Assessments of the effectiveness of AES measures to date have been based on infrequent, large-scale surveys which do not produce landscape- or intervention-specific evidence to facilitate local adaptations to the design of AES.

At 10 participating farms in the Yorkshire Dales National Park we trialled two methods that could help to evaluate the effectiveness of AES measures at regional scales. Firstly, we mailed pre-programmed acoustic recorders to be deployed on farms and used a free-to-use, automated classifier (based on a machine-learning architecture trained on large libraries of manually identified reference recordings) to produce evidence on the presence of wader species in areas under AES options. Secondly, we asked farmers to record the presence of five species of wader on a weekly basis on their farm, and provided guidance and a recording form (the Wader Calendar).

Experienced volunteer bird surveyors also carried out breeding wader surveys at the 10 participating farms to produce a validation dataset to compare with data obtained from the two trial approaches.

In 2024, the Wader Calendar was completed across the breeding season on seven of the 10 participating farms, and the acoustic recorder approach successfully gathered data across the breeding season at nine out of 10 participating farms, with one device failing for unknown reasons at the other farm in May.

The peak of a three-day rolling mean of a species’ call activity across the breeding season correlated with the peak number of pairs recorded at a site. We also tested if weekly variation in numbers recorded on the Wader Calendar correlated with numbers recorded by volunteer surveyors in those weeks, but found no statistically significant association.

The Wader Calendar was not completed every week, making seasonal inferences difficult. However, at each farm, the peak counts of individuals recorded across the breeding season from the Wader Calendar were highly comparable to peak counts obtained from the volunteer surveys.

While the methods underpinning both approaches need refinement, there is potential to use either approach to provide timely, accurate data on the presence and abundance of farmland breeding waders. Either of these approaches could generate comparable annual data that would be complementary to periodic large-scale assessments of AES, potentially in a more cost-efficient manner.

To effectively scale either approach, sufficient resources would be required for engagement with participating land managers, coordination, and collation of data. A farmer-targeted monitoring smartphone app might encourage participation in the Wader Calendar and significantly reduce data collation time but would require significant capital investment. If the acoustic approach were rolled out a larger-scale, significant ongoing resources would be needed to establish a robust approach to data storage, analyses and reporting.