Improving understanding of potato growth using crop modelling

By tamara.tenden , 18 May, 2022
Organizational Context
Name
Tamara ten Den
Chairgroup
PPS
Graduate school
PE&RC
Start date of project
Abstract

Potato is a major cash crops in the Netherlands. Therefore, there is a commercial interest in optimizing yield. One approach to aid in optimizing yield is through using crop models to understand the causes of current yield gaps. Crop models are capable of simulating potential yield (Yp), water limited (Yw) and water-and-nutrient limited yield (Yn). In this PhD, three crop models will be used. These are WOFOST, LINTUL-POTATO-DSS and Tipstar. These models differ in their photosynthesis, biomass allocation, and inclusion of tuber quality aspects.

The model WOFOST will be used to simulate yield and yield quality (dry matter content, tuber size and tubers affected by secondary growth). To this aim WOFOST will be improved and applied. The other two models will be used to improve WOFOST. Model improvement will focus on yield and on quality, photosynthesis, biomass partitioning, dry matter content of the tubers and tuber size. Effects relevant to the Netherlands will be considered. The improvements consist of updating parameters, incorporating relevant aspects of LINTUL-POTATO-DSS and Tipstar, and, if needed, model expansion to better capture relevant processes.

 We also propose to do three model applications. First is exploring the spatial differences in Yp, Yn and Yw among commercial potato regions within the Netherlands. Second, the effects of nematodes on yield will be explored by combining WOFOST to a nematode model. Third, the improved model will be used for 2050 KNMI climate scenarios with a focus on heat effects on yield and yield quality across the Netherlands.

Who's collecting the data

Tamara ten Den

Who's analysing the data

Tamara ten Den

Location short term storage

All data will be stored on my local harddisk in a folder called Thesis.

Within this Thesis folder, 

Raw and cleaned data from the field experiment will be stored in excel files. These will not be separated per chapter as the data can be used for multiple chapters. Data will be combined in one file per year with the first tab being an explanation of the file and the other tabs containing data. Data analysis will be done in R and python. Processed data will be saved and separated per chapter where possible. The processed data will be in Excel or R formats. For the chapters using data not from the field experiments raw and cleaned data will be stored in excel as much as possible while data processing will be done in R and python. The raw and cleaned experimental data will be saved in an experiment folder, while the other data will be saved in a map with the corresponding chapter number.

 

Data structure and information
 

Figure 1: file structure and file types

The files will be organised according to figure 1, each directory will also contain an archive map with older version of the files. File names will consist of the topic and date (year, month, date) or version number. The model map is temporary. This map will be used to run the model. After running the model, the data is copied to the chapter. Input files such as weather data could be used for multiple chapters. Once a chapter is completed it (all data related to it in open source format) will be uploaded to the PPS online library (https://phd.pps.wur.nl/). At the end of my PhD I will ensure that all files are within this library. The experimental data will be uploaded to agrodatacube (http://agrodatacube.wur.nl/).

 

Backup procedure

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). 

Research data with value for long term storage

Data from two years of field experiments

How to access data once you leave?

Via Pytrik Reidsma