Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.
Solid biofuel is considered as a possible substitute for coal in household heat production because of the available and sustainable raw materials, while NOx emissions from its combustion have become a serious problem. Nitrogen-containing compounds in pyrolysis products have important effects on the conversion of fuel-N into NOx-N. Understanding these converting pathways is important for the environmentally friendly use of biomass fuels. The nitrogen migration during pyrolysis of raw and acid leached maize straw at various temperatures was investigated in this study. Thermal gravimetric analysis and X-ray photoelectron spectroscopy were used to investigate the performances of thermal decomposition and pyrolysis products from samples. The main nitrogen functional groups in biomass and biochar products were N-A (amine-N/amide-N/protein-N), pyridine-N, and pyrrole-N, according to the findings. The most common gaseous NOx precursor was NH3, which was produced primarily during the conversion of N-A to pyridine-N and pyrrole-N. The formation of HCN mainly came from the secondary decomposition of heterocyclic-N at high temperatures. Before the pyrolysis temperature increased to 650 °C, more than half of the fuel-N was stored in the biochar. At the same pyrolysis temperature, acid-leached maize straw yielded more gas-N and char-N than the raw biomass. The highest char-N yield of 76.39 wt% was obtained from acid-leached maize straw (AMS) pyrolysis at 350 °C. Low pyrolysis temperature and acid-leaching treatment can help to decrease nitrogen release from stable char structure, providing support for reducing nitrogenous pollutant emissions from straw fuel.
Strawberry (Fragaria ananassa Duch.) seedlings were pretreated with hexanoic acid 2-(diethylamino)ethyl ester (DA-6) in concentrations of 0, 10, 20 and 40 mg dm -3 and then subjected to chilling and rewarming. The effects of applied DA-6 on the generation of reactive oxygen species (O 2 -, H 2 O 2 ), lipid peroxidation, proline accumulation and photosynthesis were evaluated. Pretreatment with DA-6 alleviated the inhibition of superoxide dismutase (SOD), catalase (CAT) and ascorbate peroxidase (APX) activities caused by chilling stress thus reducing O 2 -and H 2 O 2 production and lipid peroxidation in pretreated plants. DA-6 pretreatment also accelerated accumulation of proline and reduce the decrease in proline content after rewarming. DA-6 pretreatment increases maximum quantum yield of photosystem 2 (F v /F m ), actual photochemical efficiency of photosystem 2 (Φ PS2 ), photochemical quenching coefficient (qP) and net photosynthetic rate (P N ) and decreases non-photochemical quenching coefficient (qNP) of the seedlings under chilling stress. DA-6 pretreatment also increased the recovery rate of photosynthesis after rewarming.Additional key words: ascorbate peroxidase, catalase, chlorophyll fluorescence, Fragaria ananassa, net photosynthetic rate, superoxide dismutase.
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