The Application of Multiple Linear Regression and Artificial Neural Network Models for Yield Prediction of Very Early Potato Cultivars before Harvest

Magdalena Piekutowska,Gniewko Niedbała,Tomasz Wojciechowski,Tomasz Piskier,Aneta Czechowska-Kosacka,Krzysztof Pilarski,Tomasz Lenartowicz,Agnieszka A Pilarska

doi:10.3390/agronomy11050885

Abstract

Yield forecasting is a rational and scientific way of predicting future occurrences in agriculture—the level of production effects. Its main purpose is reducing the risk in the decision-making process affecting the yield in terms of quantity and quality. The aim of the following study was to generate a linear and non-linear model to forecast the tuber yield of three very early potato cultivars: Arielle, Riviera, and Viviana. In order to achieve the set goal of the study, data from the period 2010–2017 were collected, coming from official varietal experiments carried out in northern and northwestern Poland. The linear model has been created based on multiple linear regression analysis (MLR), while the non-linear model has been built using artificial neural networks (ANN). The created models can predict the yield of very early potato varieties on 20th June. Agronomic, phytophenological, and meteorological data were used to prepare the models, and the correctness of their operation was verified on the basis of separate sets of data not participating in the construction of the models. For the proper validation of the model, six forecast error metrics were used: i.e., global relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE). As a result of the conducted analyses, the forecast error results for most models did not exceed 15% of MAPE. The predictive neural model NY1 was characterized by better values of quality measures and ex post forecast errors than the regression model RY1.

Highlights

Estimating the yield of arable crops can be defined as predicting the size of the final crop yield of a given plant species, assuming that the environmental conditions characterizing a given growing season will be similar to many-year averages
Sensitivity analysis, which was carried out for the MLP 13:13-20-10-1:1 neural netvery early potato varieties harvested 40 days from full emergence was the planting date work, built on the Ap set has shown that the factor with the greatest influence on the yield
Removing of three very early potato varieties harvested 40 days from full emergence was the plantthis variable from the model would increase the cumulative error of the neural network by ing date [PLANT] defined in numbers of days from the beginning of the year (Table 5)

Summary

Introduction

Estimating the yield of arable crops can be defined as predicting the size of the final crop yield of a given plant species, assuming that the environmental conditions characterizing a given growing season will be similar to many-year averages. And accurate forecasts of crop yields before harvest are critical to the functioning of food markets. They are an important element of the organization of agricultural production [1,2,3]. In Europe (data from 2018), the average yield from potato cultivation per one hectare of plantation amounted to 22.12 tons. The potato was grown on 4.7 million hectares, with a total of 105 million tons being harvested. In Poland, in the same year, the potato was produced on 297,000 hectares, and the average yield per hectare was 25.13 tons. In Poland, it falls on average for a period of 40 days from full sprouting, i.e., 60 to 75 days from planting [6]

Methods

Results

Conclusion