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Start date: 2020 × DDC: 620 × Type: Article × Subject: Agriculture ×

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Now showing 1 - 2 of 2
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    Awareness, Experience, and Knowledge of Farming Households in Rural Bangladesh Regarding Mold Contamination of Food Crops: A Cross-Sectional Study
    (Basel : MDPI AG, 2021) Kyei, Nicholas N. A.; Waid, Jillian L; Ali, Nurshad; Gabrysch, Sabine
    Aside from specific environmental conditions, poor agricultural practices contribute to mold and thus the mycotoxin contamination of crops. This study investigated Bangladeshi farming households’ (i) awareness of and experience with mold contamination of food crops; (ii) knowledge and awareness of the timing, causes, and consequences of mold and mycotoxin contamination; and (iii) knowledge of the recommended agricultural practices for controlling and preventing mold contamination of food crops. A survey was conducted with 1280 households in rural areas of Habiganj district, Bangladesh. Basic descriptive statistics were calculated, and mixed-effects linear regression analyses were performed to examine associations between household characteristics and overall knowledge scores. The awareness of mold contamination of food crops was very high (99%; 95% CI: 98–100%) and a shared experience among households (85%; 95% CI: 80–88%). Yet, the majority (80%; 95% CI: 76–84%) demonstrated a low level of knowledge of the timing, causes, and preventive practices regarding mold contamination of crops. Knowledge scores were similar over demographic groups and better for households with more arable land. The findings suggest a generally insufficient knowledge of the conditions that favor mold contamination and the measures for preventing mold contamination of food crops. These findings underline the need for tailored interventions to promote good agricultural practices and reduce mold contamination of food crops.
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    Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data
    (Basel : MDPI, 2021) Harfenmeister, Katharina; Itzerott, Sibylle; Weltzien, Cornelia; Spengler, Daniel
    The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R2 values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R2 values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R2 values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management.