Effects of soil compaction on cereal yield

This paper reviews the works related to the effect of soil compaction on cereal yield and focuses on research of field experiments. The reasons for compaction formation are usually a combination of several types of interactions. Therefore one of the most researched topics all over the world is the changes in the soil’s physical and chemical properties to achieve sustainable cereal production conditions. Whether we are talking about soil bulk density, physical soil properties, water conductivity or electrical conductivity, or based on the results of measurements of on-line or point of soil sampling resistance testing, the fact is more and more information is at our disposal to find answers to the challenges. Thanks to precision plant production technologies (PA) these challenges can be overcome in a much more efficient way than earlier as instruments are available (geospatial technologies such as GIS, remote sensing, GPS with integrated sensors and steering systems; plant physiological models, such Decision Support System for Agrotechnology Transfer (DSSAT), which includes models for cereals etc.). The tests were carried out first of all on alteration clay and sand content in loam, sandy loam and silt loam soils. In the study we examined especially the change in natural soil compaction conditions and its effect on cereal yields. Both the literature and our own investigations have shown that the soil moisture content changes have the opposite effect in natural compaction in clay and sand content related to cereal yield. These skills would contribute to the spreading of environmental, sustainable fertilizing devoid of nitrate leaching planning and cereal yield prediction within the framework of the PA to eliminate seasonal effects

Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods

In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account

85. The impact of regulation on autonomous crop equipment in Europe

none16siGovernments required on-site human supervision for the crop robots being tested throughout Europe in 2020. For arable farms, initial evidence suggested that requiring on-site human supervision could lead to robots being used on larger farms with large fields where one human can supervise multiple robots. In spite of the technical progress in crop robotics, the legal, regulatory and policy issues around this technology have hardly been explored. Some observers suggest that, at this point, those issues may be a bigger challenge to implementation of crop robotics than the technical aspects.noneLowenberg-DeBoer, J.; Behrendt, K.; Canavari, M.; Ehlers, M.-H.; Gabriel, A.; Huang, I.; Kopfinger, S.; Lenain, R.; Meyer-Aurich, A.; Milics, G.; Olagunju, K. Oluseyi; Pedersen, S.M.; Rose, D.; Spykman, O.; Tisseyre, B.; Zdráhal, I.Lowenberg-DeBoer, J.; Behrendt, K.; Canavari, M.; Ehlers, M.-H.; Gabriel, A.; Huang, I.; Kopfinger, S.; Lenain, R.; Meyer-Aurich, A.; Milics, G.; Olagunju, K. Oluseyi; Pedersen, S.M.; Rose, D.; Spykman, O.; Tisseyre, B.; Zdráhal, I