Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R²), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R² = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R² = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.

Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth Observation (EO) data. This letter introduces an open data set for training and benchmarking machine learning methods to delineate agricultural field boundaries in polygon format. The large-scale data set consists of 439,001 field polygons divided into 62 tiles of approximately 5×5 km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized from satellite images, following a rigorous multi-step quality control process and topological consistency checks. Multi-temporal composites of Sentinel-2 (S2) images are provided to ensure cloud-free data. We conducted an experimental analysis testing a state-of-the-art Deep Learning (DL) workflow based on fully convolutional networks, contour closing, and polygonization. We anticipate that this large-scale data set will enable researchers to further enhance the delineation of agricultural fields in smallholder farms and to support the achievement of the Sustainable Development Goals (SDG). The data set can be downloaded from https://doi.org/10.17026/dans-xy6-ngg6.

An ytterbium-doped solid-core photonic bandgap fiber oscillator in an all-fiber format is investigated for high power at an extreme long wavelength. The photonic bandgap fiber is spliced with two fiber Bragg gratings to compose the cavity. The sharp-cut bandpass distributed filtering effect of the photonic bandgap fibers efficiently suppresses amplified spontaneous emission in the conventional high-gain region. Fine adjustment of the short cut-off wavelength by coiling with tighter diameter is performed to suppress parasitic lasing. A record output power of 53.6 W with a slope efficiency of 53% at 1178 nm was demonstrated.

In this study, Bacillus subtilis, Clostridium butyricum and Enterococcus faecalis were made into a probiotic complex (PC). The PC was supplemented in AA+ male broilers’ diets to investigate the effects of PC on broiler growth performance, carcass traits, blood indicators, harmful gas emissions in feces and microbiota. Three hundred and sixty 1-day-old AA+ male broilers with an average initial body weight (data) were randomly divided into 3 dietary treatments of 6 replicates each, with 20 birds per replicate. The control group (T0) was fed a basal diet, while the test groups (T1 and T2) were supplemented with 0.025 and 0.05% PC in the basal diet, respectively. The trail was 42 days. The results showed that the supplementation of 0.05% PC significantly (p < 0.05) improved average daily gain (ADG) and average daily feed intake (ADFI) of broilers from 22 to 42 days and 1–42 days. Compared to the control group, the breast rate was significantly higher in T2, and the thymic index was significantly higher than that in T1 treatment (p < 0.05). The addition of PC had no significant effects on antibody potency in broiler serum (p > 0.05), but significantly increased albumin and total protein content in serum (p < 0.05). The addition of PC reduced H₂S and NH₃ emissions in the feces; the levels of Escherichia coli and Salmonella in the feces were significantly reduced and the levels of Lactobacillus were increased. And the most significant results were achieved when PC was added at 0.05%. Correlation analysis showed a significant positive correlation (p < 0.05) between the levels of E. coli and Salmonella and the emissions of H₂S and NH₃. Conclusion: Dietary supplementation with a 0.05% probiotic complex could improve the growth performance of broilers and also reduced fecal H₂S and NH₃ emissions, as well as fecal levels of E. coli and Salmonella, and increased levels of Lactobacillus. Thus, PC made by Bacillus subtilis, Clostridium butyricum and Enterococcus faecalis is expected to be an alternative to antibiotics. And based on the results of this trial, the recommended dose for use in on-farm production was 0.05%.