Choosing multiple linear regressions for weather-based crop yield prediction with ABSOLUT v1.2 applied to the districts of Germany

Abstract

ABSOLUT v1.2 is an adaptive algorithm that uses correlations between time-aggregated weather variables and crop yields for yield prediction. In contrast to conventional regression-based yield prediction methods, a very broad range of possible input features and their combinations are exhaustively tested for maximum explanatory power. Weather variables such as temperature, precipitation, and sunshine duration are aggregated over different seasonal time periods preceding the harvest to 45 potential input features per original variable. In a first step, this large set of features is reduced to those aggregates very probably holding explanatory power for observed yields. The second, computationally demanding step evaluates predictions for all districts with all of their possible combinations. Step three selects those combinations of weather features that showed the highest predictive power across districts. Finally, the district-specific best performing regressions among these are used for actual prediction, and the results are spatially aggregated. To evaluate the new approach, ABSOLUT v1.2 is applied to predict the yields of silage maize, winter wheat, and other major crops in Germany based on two decades of data from about 300 districts. It turned out to be absolutely crucial to not only make out-of-sample predictions (solely based on data excluding the target year to predict) but to also consequently separate training and testing years in the process of feature selection. Otherwise, the prediction accuracy would be over-estimated by far. The question arises whether performances claimed for other statistical modelling examples are often upward-biased through input variable selection disregarding the out-of-sample principle.

Precision Agriculture Advisor

“…Their research represents a signi cant advancement in the eld of agricultural optimization and machine learning, demonstrating the potential of hybrid optimization techniques and ensemble learning models for enhancing crop recommendation and yield prediction accuracy. By synthesizing insights from related studies, this paper aims to contribute to the ongoing discourse on crop recommendation systems and yield prediction models, with a focus on leveraging innovative optimization algorithms and ensemble learning techniques for agricultural applications [13].…”

Implications of Neural Network as a Decision-Making Tool in Managing Kazakhstan’s Agricultural Economy

“…Most of the scientists predicted the yields of various crops using traditional econometric models. The majority methods in the studies were linear and multiple regression (Ansarifar et al, 2021;Conradt, 2022;Murugan et al, 2020;Rai et al, 2022;Sellam & Poovammal, 2016), and exponential weighted moving average (Annamalai & Johnson, 2023;Booranawong & Booranawong, 2017;Kim et al, 2020). However, the most widespread method used by scientists in forecasting crop yields was autoregressive integrated moving average (ARIMA) (Dharmaraja et al, 2020;Fan et al, 2016;Hemavathi & Prabakaran, 2018;Alani & Alhiyali, 2021;Lwaho & Ilembo, 2023;Rathod et al, 2018;Rathod et al, 2017;Senthamarai Kannan & Karuppasamy, 2020).…”