Forecasting Yield and Tuber Size of Processing Potatoes in South Africa Using the LINTUL-Potato-DSS Model

Abstract

The LINTUL-Potato-DSS model uses the linear relationship between radiation intercepted by the crop and radiation use efficiency (RUE), to calculate dry matter production. The model was developed into a yield forecasting system for processing potatoes based on long term and actual weather and crop data. The model outcome (Attainable yield, Yatt) was compared to actual yields (Yact) of a summer crop in South Africa and the ratio Yact:Yatt was used for forecasting yield in winter crops. Results showed that accurate forecasts (<20% variation between the actual and forecasted values) could be produced already early in the growing season and that for cultivar Innovator, actual and forecasted yields were well correlated (r = 0.797). Forecasted and observed yields at harvest were not significantly different at the 5% level, P = 0.637 (t-test). Forecasts of tuber number using LINTULPotato-DSS were not accurate in the present study and further research is needed on this aspect. It is concluded that the model is a valuable management tool that can be used to produce accurate 1 forecasts of tuber yield from as early as 8 weeks before final harvest. Since the model was tested with only one cultivar grown in three different growing regions of South Africa, further evaluation using different cultivars and localities is recommended.

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

“…Still, the amount of empirical data included in the modeling remains a controversial issue. In many works, the authors of the models use a lot of independent variables [3,12,44,45] or use classical prognostic models developed exclusively for potato: SUBSTOR-Potato [46,47], LINTUL-Potato-DSS Model [48], etc. In that situation, when the model tries to estimate too many unknowns for the number of observations made, the model's ability to detect real relationships is severely limited [49].…”

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

“…The simulation models forecast crop productivity by combining crop growth information including the physiological characteristics of plants, nutrient cycling, and environmental factors [ 19 ]. Over the past few decades, several simulation models have been developed for potato, such as POMOD [ 20 ], AQUACROP [ 21 ], APSIM-Potato [ 22 ], PotatoSoilWat [ 23 ], and LINTUL-Potato [ 24 ]. Although powerful, they typically require extensive crop-specific data as inputs, such as crop variety, management, and soil conditions, which are often difficult to obtain.…”

Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data

“…The growing demand for potato, coupled with the decreasing availability of fertile land for expansion, implies the need for better crop protection and management practices in order to improve crop yields [27]. Traditionally, crop growth models have been used to identify the effects of management options such as planting dates, population density, irrigation timing and frequency, as well as fertiliser applications in different environmental conditions on crop growth and yield [28,29]. In this context, crop models may prove useful for improving yield predictions for the potato processing industry [29].…”

“…Traditionally, crop growth models have been used to identify the effects of management options such as planting dates, population density, irrigation timing and frequency, as well as fertiliser applications in different environmental conditions on crop growth and yield [28,29]. In this context, crop models may prove useful for improving yield predictions for the potato processing industry [29]. These classical potato models are mainly based on the response to nitrogen fertilizer [30], temperature, and daylight [31] or the incidence of solar radiation [32] and are often used to estimate yields during the growing season.…”