Estimating the frost damage index in lettuce using UAV-based RGB and multispectral images.

Introduction: The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce. Methods: We proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed to select FDI-sensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively. Results and discussion: Digital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce group were generally higher. The MULTISURCE-GREEN-NN model with R2 of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding.

Stochastic processes drive bacterial and fungal community assembly in sustainable intensive agricultural soils of Shanghai, China.

Sustainable intensive cropping systems have been implemented for three decades in suburban agricultural districts of Shanghai, China. These human-managed soils have been developed from paleosol or alluvial soils across different regions. However, little is known about the geographical distribution patterns of microbes and microbial community assembly in the sustainable intensive soils after decades of anthropogenic disturbances. Here, we investigated the impact of local geochemical properties and geographic distance on stochastic/deterministic microbial community assembly processes using high-throughput sequencing and phylogenetic null modeling analysis. Our results showed that soil pH was the most important environmental factor determining bacterial and fungal community structure. Importantly, only soil organic matter was positively correlated with fungal α-diversity, suggesting the efficient use of carbon substrates in sustainable agricultural systems, compensating for the lack of chemical fertilization and reduced tillage in these systems. Both bacterial and fungal communities had robust distance-decay patterns, but the rate of turnover of bacterial taxa was faster than that of fungi. Variation in bacterial and fungal communities was mostly attributed to the simultaneous effects of environmental variables and spatial factors. We also mapped the spatial distributions of the dominant bacterial and fungal taxa across the sustainable agricultural fields, making it possible to forecast the responses of agricultural ecosystems to anthropogenic disturbance. Based on the patterns of the ß-nearest taxon index, this study demonstrated that stochastic processes shaped substantial bacterial and fungal community variation in sustainable intensive agricultural soils of the Shanghai suburbs. This variation may be attributed to the increasing microbial dispersal caused by hydrological connectivity in the agricultural fields or the release from environmental stress and weakened environmental filtering across the suitable pH range preferable for most soil microbes. These results unveil assembly mechanisms of soil microbial community after several decades of sustainable intensive management, and contribute to understand the role of microbes in ecosystems in establishing a functional equilibrium which may enable sustainability to be preserved.

Isotope chemometrics determines farming methods and geographical origin of vegetables from Yangtze River Delta Region, China.

Shanghai city has encountered possible food fraud regarding the geographical mislabeling of vegetables for economic gain. A combination of δ13C, δ15N, δ2H and δ18O values and partial least squares discrimination analysis and support vector machine (SVM) methods were used for the first time to assess farming methods and determine the origin of vegetables from Shanghai city, Anhui and Zhejiang provinces. The results showed that 65.8% of Shanghai vegetables, 38.2% of Anhui vegetables and 23.6% of Zhejiang vegetables appeared to be grown using green or organic farming methods. The optimal discriminant model was obtained using SVM with a predictive accuracy of 100% for Shanghai vegetables. Zhejiang vegetables had a predictive accuracy of 91.7%, while it was difficult to distinguish Anhui vegetables from Shanghai or Zhejiang vegetables. Therefore, this study provided a useful method to identify vegetable farming methods and discriminate vegetables from Shanghai and Zhejiang.

Microplastic pollution in rice-fish co-culture system: A report of three farmland stations in Shanghai, China.

Microplastics are emerging contaminants of increasing concern. Despite the occurrence of microplastics in farmland soils, the knowledge on microplastics in rice-fish co-culture ecosystems is limited. In this study, we investigated the distribution of microplastics in three rice-fish culture stations in Shanghai. During non-rice and rice-planting periods, microplastics in water, soils and aquatic animals (eel, loach and crayfish) were systematically assayed using methods of NaCl density extraction, H2O2 digestion and micro-fourier transform infrared spectroscopy. Results showed that average microplastic abundances were 0.4 ±â€¯0.1 items L-1, 10.3 ±â€¯2.2 items kg-1, 1.7 ±â€¯0.5 items individual-1 in water, soils and aquatic animal samples, respectively. We found an increasing trend in microplastic abundances in water, soil and animal samples from non-rice period to rice-planting period. Almost all of microplastics were found in digestive tracts of animals. Major microplastics were small (<1 mm) polyethylene and polypropylene fibers, with color of white and translucent. Size, shape, color and polymer type distributions of microplastics were similarly found in environmental and animal samples. Moreover, microplastic abundances in aquatic animals correlated to abundance in farmland soils. This study, for the first time, reveals the occurrence and characteristics of microplastic pollution in rice-fish culture ecosystem which suggests the potential ecological risks of microplastics in the agroecosystem.