Potato Research

The European Association for Potato Research (EAPR) publishes a journal, Potato Research, which includes original scientific contributions on both fundamental and applied research on potatoes, review articles and topics of general interest. It also contains Association news and reports on section meetings (including abstracts of papers presented).
A volume of the Journal (usually approximately 450 pages), consists of four issues, published in the same year. Submission of manuscripts is exclusively online via a web-based electronic submission and tracking system available at https://www.editorialmanager.com/potr

EAPR Members have online access to Potato Research (use the EAPR member login page)
A list of addresses of editors of ‘Potato Research’
Contents of ‘Potato Research’ can be found at Springerlink
Instructions for authors
Any comments regarding the publication and distribution of Potato Research should be addressed to EAPR

Springer Link

https://link.springer.com/journal/11540

Latest Issues

NARX Model for Potato Price Prediction Utilising Multimarket Information
17th January, 2025
Abstract

Agricultural price series are typically characterised by nonlinearity, nonstationarity, and inherent complexity, which make precise forecasting a challenging task. Artificial neural networks (ANNs) have demonstrated superior performance over traditional statistical methods in modelling such series. Incorporating auxiliary information has the potential to enhance forecasting accuracy. Amongst ANN-based architectures, the nonlinear autoregressive model with exogenous input (NARX) network stands out as a dynamic neural architecture that effectively leverages auxiliary information to develop data-driven nonlinear forecasting models for systems exhibiting nonlinearity. The NARX network addresses critical limitations such as the vanishing gradient problem, exhibits faster convergence, and offers superior generalisation capabilities compared to alternative networks. This study evaluates the predictive performance of the NARX model in forecasting potato price series for Delhi and Guwahati markets by incorporating prices from other markets with varying correlation values as exogenous inputs. Empirical findings indicate that the NARX model outperforms both the autoregressive integrated moving average with exogenous variable (ARIMAX) and time delay neural network (TDNN) models in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), and directional accuracy statistics. Additionally, the results affirm that the accuracy gains achieved by the NARX model are positively correlated with the degree of correlation between the target and exogenous series. Furthermore, the Diebold-Mariano test confirms the statistically significant improvement in forecasting accuracy offered by the NARX model over competing approaches.
See full article
Novel Approaches for Potato Drying: Intermittent Microwave Drying with Pretreatments (Ethanol, Hot Water Blanching, and Citric Acid), Drying and Quality Properties, and Energy Consumption
16th January, 2025
Abstract

This study investigates the effects of intermittent microwave drying (IMD) on the drying characteristics, energy consumption, and quality of potato slices, using different power levels (240 W, 400 W, 640 W) and pretreatments (ethanol, hot water blanching, citric acid). IMD significantly reduced drying time, with 400 W and 640 W power levels shortening the drying time by 54.55% and 74.55%, respectively. Ethanol pretreatment (100%) further reduced drying time by 30.91%. The effective moisture diffusivity (Deff) ranged from 3.29 × 10−9 to 1.10 × 10−8 m2 s−1, increasing with higher power and pretreatment application, while the drying coefficients (S) improved, reflecting enhanced moisture removal rates. On the other hand, artificial neural network (ANN) modeling demonstrated superior predictive accuracy for drying behavior, with RMSE values as low as 0.0008 and R2 values near 0.9999 in comparison to Midilli and Kucuk and Page models. Energy efficiency improved with higher power. As the specific moisture extraction rate (SMER) increased, while the specific energy consumption (SEC) decreased. IMD preserved the color of potato slices, with no significant change in lightness (L*), while slight increases in redness (a*), and yellowness (b*) were noted. Shrinkage, particularly in thickness, was influenced by power levels and pretreatments, with ethanol pretreatment causing the greatest shrinkage due to cell disruption. In conclusion, IMD, especially when combined with ethanol pretreatment, enhances drying efficiency and product quality, offering a viable method for potato processing.
See full article
First Detection of Arsenophonus in Potato Crop in Switzerland: A Threat for the Processing Industry?
15th January, 2025
Abstract

The phloem-restricted phytopathogenic bacterium Candidatus Arsenophonus phytopathogenicus (Ap) causes the “syndrome basses richesses” (SBR), which has recently emerged as a major burden for sugar beets in several countries of Western Europe. Here, we report the first identification of Ap and its planthopper vector Pentastiridius leporinus in potato fields in Switzerland in 2023. The bacterium was detected in potato plants and tubers exhibiting phytoplasma-like symptoms and collected from cantons currently experiencing SBR outbreaks. Although our analyses indicate the absence of the Stolbur phytoplasma, the pathogenicity of Ap in potato remains unclear, a fortiori in the context of the poor phytosanitary status of crops in Switzerland in 2023. In infected tubers, we show that Ap can be detected after dormancy from the stem end to the emerging sprouts with decreasing titers. Importantly, Ap might induce threadlike sprouts and the browning of the flesh upon frying, raising strong concerns for varieties marketed for chips production. Altogether, our results align with recent studies performed in Germany, highlighting the host shift of Ap and its vector from sugar beet to potato crops. Our data should raise awareness in other European countries where the presence of the bacterium has been recently described.
See full article
Correlation-based network analysis combined with machine learning techniques highlights rosmarinic acid biosynthesis activation in tubers of potato plants corresponding to water recovery treatment
14th January, 2025
Abstract

Water-deficit, as a consequence of elongated drought periods due to current climate trends, is one of the most imminent abiotic stresses limiting crop productivity worldwide. Also, the production of potato—the world’s fourth most important crop—is challenged by the effects of water-deficit. To investigate metabolic pathway activity in potato tubers subjected to water-deficit, a correlation-based network analysis combined with machine learning techniques approach was conducted on a subset of samples of a potato association panel, grown in an augmented block design under normal irrigation and water-deficit conditions in Ica, Peru. Metabolic profiles for all samples of the subset were generated using a cold injection GC-TOF-MS platform unequivocally identifying 685 metabolites. Samples were separated into positive spectrum (Spectrum [+]) and negative spectrum (Spectrum [−]) groups based on their projections on principal component 2 (PC2) using principal component analysis. Correlation networks showed a strong discrepancy of edge number between the Spectrum [+] and Spectrum [−] correlation network (143,478 vs. 185,565 edges). Correlation-based network analysis combined with machine learning techniques highlighted the activity of metabolic pathways centred around tyrosine and α-ketoglutarate suggestive for an increased activity of the rosmarinic acid biosynthesis I and II pathways in potato tubers. Our study suggests that potato tubers cope with drought stress by rewiring their metabolic network and boosting rosmarinic acid biosynthesis to counteract ROS activity and by that ensuring the onset of the next life cycle.
See full article
Forecasting Potato Prices in Agra: Comparison of Linear Time Series Statistical vs. Neural Network Models
13th January, 2025
Abstract

Potato is grown in many states in India like Uttar Pradesh, West Bengal, Bihar, Maharashtra, Karnataka and Gujarat, but its prices are volatile during the post-harvest period. This poses challenges for farmers, consumers and policymakers. Farmers, often needing immediate cash, sell at lower prices, while a lack of timely market intelligence hampers strategic selling and policy decisions, highlighting the need for accurate price forecasting. The selection of the Agra market was done as it has the highest total arrivals of potato among all other markets of India. The weekly price data from January 2003 to December 2023 were utilized for price forecasting, where comparisons were made between traditional statistical models like ARIMA and SARIMA, and neural network models such as ANN and LSTM. It was found that the long short-term memory (LSTM) model outperformed other models, showing the lowest mean absolute percentage error (MAPE) of 7.99% and the highest accuracy. The out-of-sample forecasts for the Agra market using LSTM showed the lowest MAPE and root mean square error (RMSE) values, with forecasted prices for the months of 2024 ranging from Rs 613 to Rs 1702 per quintal. This accurate forecasting of potato prices plays a crucial role in optimizing supply chain management, supporting informed decisions on procurement, storage, and distribution. It enables farmers to sell strategically, helping to mitigate price volatility.
See full article

Latest Issues

NARX Model for Potato Price Prediction Utilising Multimarket Information

Abstract

Novel Approaches for Potato Drying: Intermittent Microwave Drying with Pretreatments (Ethanol, Hot Water Blanching, and Citric Acid), Drying and Quality Properties, and Energy Consumption

Abstract

First Detection of Arsenophonus in Potato Crop in Switzerland: A Threat for the Processing Industry?

Abstract

Correlation-based network analysis combined with machine learning techniques highlights rosmarinic acid biosynthesis activation in tubers of potato plants corresponding to water recovery treatment

Abstract

Forecasting Potato Prices in Agra: Comparison of Linear Time Series Statistical vs. Neural Network Models

Abstract

EAPR Sustaining Members