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• The effect of soil tillage and nutrient supply on maize yield based on multi-year experimental results

  Péter Ragán

  Tamás Rátonyi

  Péter Fejér

  Endre Harsányi

  Andrea Balláné Kovács

  Costa Gumisiriya

  János Nagy

  István Sojnóczki

  András Tamás

  Views:

  80

  To optimize maize (Zea mays) yield, soil tillage and nutrient supply play a key role. The application of appropriate soil tillage techniques and the precise application of nutrients can contribute to increasing yield, maintaining plant health, and developing sustainable agricultural practices. The aim of the study was to analyse the long-term yield performance of maize hybrids under different nutrient supply levels and basic tillage methods. According to the repeated measurement model, soil tillage, fertilization, and crop year had a significant (p<0.001) effect on maize yield. The integrated approach allows for the optimization of yield and the development of sustainable agricultural practices. Reduced soil tillage methods reduce soil erosion and improve soil biological activity.

• Predicting maize yield with a multilayer perceptron (MLP) model using multivariate field data

  Péter Fejér

  Adrienn Széles

  Péter Ragán

  Csaba Juhász

  Éva Horváth

  Tamás Rátonyi

  Views:

  99

  This study presents the findings of a multi-year maize field trial conducted on experimental plots between 2017 and 2019, focusing on the application of machine learning techniques to enhance yield prediction accuracy. A multilayer perceptron (MLP) neural network was employed to model the effects of agronomic treatments, environmental variation, and compositional traits. Six distinct modeling scenarios were developed to explore different combinations of input variables, with the grain yield of maize serving as the sole output parameter. These scenarios range from treatment-only models to those incorporating detailed quality and compositional data. The primary objective was to evaluate how well MLP models can capture the complex, nonlinear relationships influencing yield under varying conditions. The findings provide valuable insight into the role of machine learning in supporting decision-making for sustainable crop production, especially under diverse technological and environmental settings. The approach demonstrated here offers a foundation for more adaptable, data-driven strategies in agronomic optimization.