Three treatments were tested with four replications at RCBD, namely cassava monoculture, corn twice under cassava intercropping, and corn with corn monoculture where the second corn was planted 5 days after harvested. Planting distance of cassava in monoculture was 100x100 cm, and in intercropping was 200x50 cm, by which corn was planted in 50x30 cm, 1 seed per hole. All plots received dairy cattle manure 5 t haE-1. Fertilizer applied for cassava was Urea 100 kg haE-1, Ponska 200 kg haE-1 and KCl 100 kg haE-1 split into two applications two weeks after cassava planting, then when first of corn was harvested. Corn fertilization was Ponska 150 kg haE-1 + Urea 100 kg haE-1 every 3 months and split into two applications: 1 week and 4 weeks after corn was planted. Cassava and twice corn fertilization was Urea 300 kg haE-1 + Phonska 400 kg haE-1 + KCl 100 kg haE-1. Results showed that cassava monoculture can be improved by inserting of twice corn in between rows. Under sole cropping cassava, the B/C ratio was the highest 1.61, however with incorporating twice of corn the B/C ratio was 1.58. Intercropping cassava and twice of corn resulted in the Land Equivalent Ratio 2.146. By intercropping cassava with corn twice at the end of dry season, there was an opportunity to find corn biomass for dairy cattle. Under intercropping, cassava was able to produce 21.5 t haE-1 of fresh roots, while first and second of corn produced fresh biomass of 34 t haE-1 and 28 t haE-1 respectively.

Nitrogen management strategy in plant growth period based on chlorophyll content evaluation in plant can improve nitrogen usage efficiency and reduce environmental contamination. This study is aimed to determine the impact of different nitrogen and sulphur fertilizers rates on dynamics of chlorophyll content in winter wheat during vegetative growth and to determine the relationship between nitrogen and chlorophyll content and grain yield of winter wheat. Field trial involving a winter wheat (Triticum aestivum L.) variety ‘Kranich’ was conducted at the LUA Research and Study Farm Vecauce during a three-year period (2012-2015). The treatments were 0, 85, 153, 175+S21, 175 (in 2015), 187 N kg haE−1 and different nitrogen norms according to chlorophyll meter Yara N-tester (Konica Minolta Ltd.) data: 180, 150, 205 N kg haE−1 depending on the year. The results of the trial show that the maximum chlorophyll content in different plant parts was observed at the end of flowering stage. The chlorophyll content depended on the level of mineral fertilisation. The highest chlorophyll content in leaves, stems and ears was obtained by using additional sulphur in two trial years. Usage of chlorophyll meter Yara N-tester obtained the highest chlorophyll content in all analysed plant parts in one trial year. Chlorophyll content was significantly dependant on plant growth stage in stems in all trial years, in leaves in two trial years, and in ears in one year. Nitrogen fertilization significantly affected chlorophyll content in leaves and stems in one trial year. Close positive correlation was observed between grain yield and wheat plant chlorophyll content and average nitrogen concentration at the end of flowering stage in all three trial years.

Global overpopulation necessitates increased crop yields, yet available arable land is limited. The study compares and evaluates the performance of three machine learning algorithms—Random Forest (RF), Extra Trees (ET), and Artificial Neural Network (ANN)—in crop yield prediction. Using 28,242 samples with seven features from 101 countries, we evaluated these models based on Mean Absolute Error (MAE), R-squared (R^2), and Mean Squared Error (MSE). The ET regression model demonstrated superior performance, achieving an MAE of 5249.03, the lowest among the models tested. Despite having the highest R^2 value of 0.9873, the ANN exhibited higher MAE and MSE values, indicating less reliability. The RF model showed intermediate results. With a prediction accuracy of 97.5%, the ET model proved to be the most effective for crop yield prediction, achieving the highest accuracy reported to date. Future research should explore more advanced algorithms and larger datasets to validate these findings further.

The common control method of leaf blotches is the application of fungicides; however, the results of trials are inconsistent. The aim of the present study was to evaluate the impact of the fungicide treatment intensity on the severity of leaf blotches and to assess the correlation between the development of diseases and winter wheat (Triticum aestivum) yield in 2018–2021. The development of diseases was evaluated regularly. In this study, the severity of leaf blotches at the medium milk ripening (GS 75–77) was used. Tan spot dominated in 2018, 2019, and 2021, but Septoria tritici blotch dominated in 2020. Fungicides significantly decreased the severity of tan spot and Septoria tritici blotch, but the efficacy of used fungicide treatment schemes differed during trial years – it was more pronounced under higher pressures of leaf blotches. In the year with a high severity of Septoria tritici blotch, the best efficacy was obtained when fungicide was used two times (at GS 32–33 and GS 55–59), but for tan spot – when the dose of fungicide was done in two or three applications. Usage of fungicides increased wheat yield only under high pressure of diseases. A strong significant correlation between the severity of Septoria tritici blotch at GS 75–77 and grain yields was established only in 2020, when the severity of the disease was higher.