PhD Field and farm monitoring for sustainable potato production

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'Yield gap analysis for sustainable potato production' (Potato Gap NL) is a project funded by NWO and Holland Innovative Potato, an initiative of 10 companies active in the potato value chain and prominent global players in the fields of potato breeding and processing. Increases in potato yields in the Netherlands have been relatively small compared to other crops. The yield gap, i.e. the difference between potential and actual yield, is significant, and yields vary largely across fields (even within one farm) and years. In this project, we aim to analyse potential and actual potato yields and yield gaps at field and farm level across the Netherlands. Insight will be integrated in a decision-support system (DSS) that can benchmark potato yields and provide insight in the causes of yield gaps at individual field level while accounting for farm specific conditions. Trade-offs with tuber quality, resource use efficiency and environmental impacts will be included. Three main steps will be followed: 1) improvement of potato growth models to assess yield and quality, 2) field and farm monitoring and analysis of actual yields, quality, resource use efficiency and environmental impacts ('crop performance'), 3) development of a DSS. Experiments will be conducted under step 1 for calibration and validation of the model, and under step 2 for analysis of specific agronomic variables influencing yield. We seek a highly motivated PhD candidate who can work on the second step. In the second step, the aim is to perform statistical analyses in order to analyse the influence of a range of agronomic and soil variables and their interactions on crop performance. You will a) analyse and explain variability in crop performance across fields on farms in the Netherlands using frontier analysis, b) analyse and explain variability in crop performance across fields within farms, using innovative data science approaches, and c) analyse the influence on crop performance of single agronomic variables (e.g. seed quality and soil quality) and their interactions in an experimental setting. Experience with performing surveys, data analysis, statistical techniques and field experiments are essential.

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