The average yield gap, i.e. the difference between actual and potential yield, of ware and starch potatoes in the Netherlands is estimated respectively at 29 and 40 %. Yield variability is large with ranges reported from 30 to 100 t ha-1. Hence, there is potential to increase potato production, reduce environmental impact and increase cost-benefit ratios for farmers. This research aims at identifying key yield defining, limiting and reducing factors that explain yield variability at three different spatial scales. With availability of knowledge intensive machinery, farmers recently started to actively collect and use data. In addition, new data collection methods, such as remote sensing, and analysis approaches have become available to improve understanding and providing decision support to farmers. The first research question of this research aims at identifying spatiotemporal yield variation among farms, among fields within farms and within fields, using historical yield data and geospatial statistical techniques. Question 2 will assess the yield response to P and K fertilizer application on top of farmers’ nutrient application rates. The aims of question 3 and 4 are to identify key factors that can explain yield variability, respectively among farms and among fields within farms, using various statistical approaches. Research question 5 focusses on the relation between within field yield variability and topography as a driver for differences in soil moisture, using farmer measured spatial yield data and publicly available geospatial data. Together, the answers to these questions provide up-to-date insight on factors explaining yield variability, which can be used to develop a decision-support system for farmers.
Organizational Context
Roles
Partly the PhD candidate, with help of students and colleagues. Part of the used data is collected by Wageningen Economic Research. Part of the data is collected by farmers
the PhD candidate with feedback from supervisors
Short and long term storage
All data will be stored on my local harddisk in a folder called Thesis.
Within this Thesis folder, I'll create per chapter the folders: Data, Model, Paper and Scripts. The Data folder has two sub-folders called: Raw and Processed.
Folder contents:
- Data - Raw sub-folder: Contains all raw data and meta-data (a description of your data).
- Data - Processed sub-folder: Contains all processed data.
- Model folder: Complete listing of the model and the model results & analysis.
- Paper folder: Text of a chapter / paper.
- Scripts folder: Contains all scripts used.
The complete content of my local Thesis folder will be stored on my YoDa-drive.
During periods I'm abroad, I'll backup the complete content of my local Thesis folder to a Dropbox or MS-Onedrive Thesis folder and share the contents with my supervisor(s).
A two year experiment among 96 potato fields have been conducted assessing actual yield (variability). This data might be valuable for long-term storage as it is a large dataset which can be used as a comparison for later studies.
Data collected by farmers and by Wageningen Economic Research as others are owners of these data
Sharing and Ownership
For all data and outputs there is the intention to make these publicly available. There are however several cases where this cannot happen. Part of the data is from potato processing industry companies. It will depend on the companies whether they would allow the data to be published open access. Part of the data is owned by farmers. The farmers allow us to use the data and to share the outputs, but they do not always allow the data to be published open access. For self-collected data there will be the intention to make this publicly available. This data contains however also private information about farms, therefore this data will be anonymized.
If a chapter is published – and data is not used in another chapter – the data will be uploaded to the portal of the PPS chair group (https://phd.pps.wur.nl/). If data cannot be published open access there is an option to restrict access to the data. In those cases there will be a contact person to request access to the data. If possible data will be uploaded to the agrodatacube repository (http://agrodatacube.wur.nl/) or published in the Open Data Journal for Agricultural Research (www.odjar.org)