Browsing by Author "Gozdowski, Dariusz"

Now showing 1 - 5 of 5
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    Adaptive Agronomic Strategies for Enhancing Cereal Yield Resilience Under Changing Climate in Poland
    (MDPI, 2024) Wójcik-Gront, Elżbieta; Gozdowski, Dariusz; Pudełko, Rafał; Lenartowicz, Tomasz
    Climate-driven changes have raised concerns about their long-term impacts on the yield resilience of cereal crops. This issue is critical in Poland as it affects major cereal crops like winter triticale, spring wheat, winter wheat, spring barley, and winter barley. This study investigates how soil nutrient profiles, fertilization practices, and crop management conditions influence the yield resilience of key cereal crops over a thirteen-year period (2009–2022) in the context of changing climate expressed as varying Climatic Water Balance. Data from 47 locations provided by the Research Centre for Cultivar Testing were analyzed to assess the combined effects of agronomic practices and climate-related water availability on crop performance. Yield outcomes under moderate and enhanced management practices were contrasted using Classification and Regression Trees to evaluate the relationships between yield variations and agronomic actors, including soil pH, nitrogen, phosphorus, potassium fertilization, and levels of phosphorus, potassium, and magnesium in the soil. The study found a downward trend in Climatic Water Balance, highlighting the increasing influence of climate change on regional water resources. Crop yields responded positively to increased agricultural inputs, especially nitrogen. Optimal soil pH and medium phosphorus levels were identified as crucial for maximizing yield. The findings underscore the importance of tailored nutrient management and adaptive strategies to mitigate the adverse effects of climate variability on cereal production. The results provide insights for field crop research and practical approaches to sustain cereal production in changing climatic conditions.
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    Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models
    (MDPI, 2025) Okupska, Edyta; Gozdowski, Dariusz; Pudełko, Rafał; Wójcik-Gront, Elżbieta
    This study performed in-season yield prediction, about 2–3 months before theharvest, for cereals and rapeseed at the province level in Poland for 2009–2024. Variousmodels were employed, including machine learning algorithms and multiple linear regression.The satellite-derived normalized difference vegetation index (NDVI) and climaticwater balance (CWB), calculated using meteorological data, were treated as predictors ofcrop yield. The accuracy of the models was compared to identify the optimal approach.The strongest correlation coefficients with crop yield were observed for the NDVI at thebeginning of March, ranging from 0.454 for rapeseed to 0.503 for rye. Depending on thecrop, the highest R2 values were observed for different prediction models, ranging from0.654 for rapeseed based on the random forest model to 0.777 for basic cereals based onlinear regression. The random forest model was best for rapeseed yield, while for cereal, thebest prediction was observed for multiple linear regression or neural network models. Forthe studied crops, all models had mean absolute errors and root mean squared errors notexceeding 6 dt/ha, which is relatively small because it is under 20% of the mean yield. Forthe best models, in most cases, relative errors were not higher than 10% of the mean yield.The results proved that linear regression and machine learning models are characterized bysimilar predictions, likely due to the relatively small sample size (256 observations).
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    Możliwości wykorzystania Systemu Informacji Geograficznej do weryfikacji wniosków o płatności powierzchniowe
    (Instytut Uprawy Nawożenia i Gleboznawstwa Państwowy Instytut Badawczy, 2008) Sioma, Sławomir; Gozdowski, Dariusz
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    MULTIVARIATE ANALYSIS OF PHENOTYPICAL VARIABILITY OF SELECTED MORPHOLOGICAL AND AGRONOMICAL TRAITS IN LOCAL FORMS OF WINTER RYE
    (Instytut Uprawy Nawożenia i Gleboznawstwa – Państwowy Instytut Badawczy w Puławach, 2013) Kubicka-Matusiewicz, Helena; Gozdowski, Dariusz; Puchalski, Jerzy; Wiśniewski, Marcin
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    Szanse i ograniczenia dla rolnictwa ekologicznego na Nizinie Mazowieckiej
    (Instytut Uprawy Nawożenia i Gleboznawstwa – Państwowy Instytut Badawczy w Puławach) Kucińska, Katarzyna; Artyszak, Arkadiusz; Gozdowski, Dariusz