Crop Pollination Database

Funded by:
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Coordinator: Ignasi Bartomeus (EBD-CSIC)

In recent years we have made huge advances in our understanding of the effects of managed and wild pollinators on crop yield. However, our ability to predict visitation rates and crop yield are still limited due to the huge variation observed among crops, years and environments. To advance in this direction we want to (i) compile a dynamic and open database on crop pollination data published as a data paper with all data contributors as co-authors and (ii) use this database to compare the predictive ability of data-driven, statistical and mechanistic models on crop pollination.

We want to reach every researcher working on the pollination of different crops to create a dynamic crop pollination database. Basically, we are looking for studies measuring pollinators within the crop (visitation rates or abundances) for at least five fields and ideally a measure of crop production (tonnes per hectare, fruit set, …). Here you can download the template with the requested data. Data should be sent using our semi-automated workflow (See Read Me, How to contribute: Queries can be sent to Alfonso Allen-Perkins () or Ignasi Bartomeus ().

How will your data be used?

  1. Open data papers allow us to gather global standardized data on a particular topic, thus constituting a huge resource for the scientific community. These papers are highly cited products. Further, the outcomes of the analysis that can be done with them, especially when merged with other types of data, are unpredictable. This approach also represents a great way to give credit to everyone involved in the collection of data, as all data collectors are co-authors of the resulting data paper. In addition, we will encourage the citation of each individual study when only those are used. Examples of such efforts are the predicts database, TRY or the meta-community database. We think that by building a worldwide crop pollination database (which will keep growing in the future years) we can stimulate the advance of science and give proper credit to all data holders.

  2. As mentioned above, we plan to use this data to compare the predictive ability of different modeling techniques. Prediction is the next frontier in ecology and if successful, our work can have a great impact. Given this will be the first use of the database, we would like to invite interested data holders that also want to get involved in the manuscript to co-author this first global paper. We expect the paper to be published in 2022.

We hope to have you on board. Do not hesitate to ask any relevant question.

The Data:

We have already compiled data from > 190 study systems worldwide, comprising > 3000 fields from 49 different crop species. You can see there are some geographical gaps, share your data to change this!


Papers in preparation:

1- CropPol: a dynamic, open and global database on crop pollination. Allen-Perkins et al. In preparation. Expected publication date: 2021.

2- Modelling pollination services: A model performance comparision. In preparation.

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