Search

Background and Aims: This research investigates vegetation dynamics in the Gavkhouni catchment from 2001 to 2021, focusing on the spring season. The aim is to analyse the relationship between aridity, vegetation, and rainfall. Moreover, additional emphasis was placed on exploring the impact of these dynamics on agricultural land cover thereby contributing to our understanding of the environmental dynamics in the Gavkhouni catchment. Methods: The study made use of MODIS data, including the Enhanced Vegetation Index and Vegetation Condition Index, along with monthly rainfall statistics from Chirps. Analytical methods include time series analyses using correlation and regression analysis. Results: Throughout the study period, the average spring vegetation cover was 9276.33 km². The years 2001 and 2018 had the lowest degree of vegetation (15.53, and 17.3% of the watershed area). Conversely, 2013, 2019, and 2020 had the most coverage (27.4, 26.8, and 26.3%). The Enhanced Vegetation Index highlighted the arid years (2001, 2008, 2011, and 2018) and the years with the lowest drought prevalence (2006, 2007, 2010, 2013). Enhanced Vegetation Index correlated with spring rainfall. Cropland cover declined over the study period, and a close cor- relation was found between winter rainfall and spring agricultural coverage. [ABSTRACT FROM AUTHOR]

This study explores the feasibility of analyzing soil organic carbon (SOC) in carbonate-rich soils using visible near-infrared spectroscopy (VIS-NIR). Employing a combination of datasets, feature groups, variable selection methods, and regression models, 22 modeling pipelines were developed. Spectral data and spectral data combined with carbonate contents were used as datasets, while raw reflectance, first-derivative (FD) reflectance, and second-derivative (SD) reflectance constituted the feature groups. The variable selection methods included Spearman correlation, Variable Importance in Projection (VIP), and Random Frog (Rfrog), while Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were the regression models. The obtained results indicated that the FD preprocessing method combined with RF, results in the model that is sufficiently robust and stable to be applied to soils rich in calcium carbonate. [ABSTRACT FROM AUTHOR]

This paper presents the results of a survey conducted electronically in the years 2020-2022 within the framework of the AGRICORE Horizon project. It concerned the Agri-Environment-Climate Measure M10 within the Rural Development Programme 2014-2020 and aimed to quantify the impact of its effects on environmental and climatic policy implementation at a national level according to the perceptions of Polish farmers. The representativeness of the scrutinized population was checked using general data from the Polish Statistics Office. The results of our study show a positive perception of M10 by the participating farmers. The majority of them observed the income progress of their activities despite the increased workload connected with programme implementation and the increased costs associated with some of the declared activities. The innovation activities of the M10 participants were directed mainly at sustainable agriculture and protecting the environment. The respondents who did not decide to participate in M10 most frequently explained themselves by noting a lack of information about the programme, bureaucratic limitations, or doubts concerning the profitability of participation. The results of the study suggest that during the implementation of future EU agri-environmental measures, more attention should be paid to administrative and legal activities at the national level which may improve the perception of the programme. [ABSTRACT FROM AUTHOR]

The implementation of precision farming technologies into agricultural practice requires, among other things, precise determination of the extent and intensity of insect infestation in the farmer' fields. Manual insect identification is time‐consuming and has low efficiency, especially for large fields. Therefore, scientists and practitioners devote much effort to the automatization of this process. There are two complementary approaches to insect identification: (i) direct, in which the insect (ultimately the species) is determined, and (ii) indirect, in which the damage caused by the insects is monitored and forms the basis on which to formulate the information about insect infestation. A mini‐review of both approaches is presented in this work. Additionally, the advantages and disadvantages of each are briefly described. Methods of insect identification are still characterized by relatively small selectivity and efficiency, therefore it is necessary to keep searching for new methods and improve the development of existing ones. The goal of such systems should be to work in real time and be inexpensive to run, enabling widespread use amongst farmers. A possible solution seems to be integrating various techniques (sensor fusion) into a single measurement system. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]