Organic fertilizers are generally thought to be an effective way to sustain soil fertility and plant growth. To promote the productivity of chrysanthemum, five sources of liquid organic fertilizers (L1–L5), as well as a chemical fertilizer, were applied at an early stage of the growth cycle to investigate their effects on plant growth. In the short-term pot experiment, the liquid organic fertilizers significantly promoted root and aboveground growth by 10.2–77.8% and 10.7–33.3%, respectively, compared with the chemical fertilizer. The order of growth promotion was: L1 (shrimp extracts) > L2 (plant decomposition) > L4 (seaweed extracts)/L5 (fish extracts) > L3 (vermicompost). Morphological and chemical analyses indicated that, compared with other organic fertilizers, the treatment with shrimp extract (L1) produced the greatest increases in root dry weight, total length, surface area, volume, tips, and thick root length, respectively. Furthermore, the shrimp extract treatment significantly increased the nutrient contents and altered the soil’s functional microbial community at the rhizospheric level compared with the chemical fertilizer treatment. Thus, the shrimp extract liquid organic fertilizer could be part of an effective alternative to chemical fertilization during the early stage of chrysanthemum growth.

Plant growth regulators are known to exert strong influences on plant performance under abiotic stress, including exposure to high nitrate, as occurs commonly in intensive vegetable production. However, direct comparative evaluations of growth regulators under otherwise identical conditions in major crop species are scarce. In this study, tomato (Solanum lycopersicum L.) was used as a model crop, and the roles of four common exogenously applied plant growth regulators (MT, melatonin; SA, salicylic acid; HA, humic acid; SNP, sodium nitroprusside) in regulating crop growth were studied under high-nitrate stress. We provide a particular focus on root system architecture and root physiological responses. Our data show that all four growth regulators improve tomato tolerance under high nitrate, but that this occurs to differing extents and via differing mechanisms. Optimal concentrations of MT, SA, HA, and SNP were 50 μmol L^–1, 25 μmol L^–1, 25 mg L^–1, and 50 μmol L^–1, respectively. MT and SNP produced the strongest effects. MT enhanced root growth while SNP enhanced above-ground growth. Growth of coarse and thin lateral roots was significantly improved. Furthermore, an enhancement of root vitality and metabolism, improved integrity of root cell membranes, and an increase in antioxidant enzyme activities were found, but regulatory mechanisms were different for each growth regulator. Our results show that in particular the application of MT and SNP can improve growth of tomato in intensive vegetable production under high-nitrate stress and that root growth stimulation is of special importance in procuring these beneficial effects.

Efficient and precise yield prediction is critical to optimize cabbage yields and guide fertilizer application. A two-year field experiment was conducted to establish a yield prediction model for cabbage by using the Greenseeker hand-held optical sensor. Two cabbage cultivars (Jianbao and Pingbao) were used and Jianbao cultivar was grown for 2 consecutive seasons but Pingbao was only grown in the second season. Four chemical nitrogen application rates were implemented: 0, 80, 140, and 200 kg·N·ha⁻¹. Normalized difference vegetation index (NDVI) was collected 20, 50, 70, 80, 90, 100, 110, 120, 130, and 140 days after transplanting (DAT). Pearson correlation analysis and regression analysis were performed to identify the relationship between the NDVI measurements and harvested yields of cabbage. NDVI measurements obtained at 110 DAT were significantly correlated to yield and explained 87–89% and 75–82% of the cabbage yield variation of Jianbao cultivar over the two-year experiment and 77–81% of the yield variability of Pingbao cultivar. Adjusting the yield prediction models with CGDD (cumulative growing degree days) could make remarkable improvement to the accuracy of the prediction model and increase the determination coefficient to 0.82, while the modification with DFP (days from transplanting when GDD > 0) values did not. The integrated exponential yield prediction equation was better than linear or quadratic functions and could accurately make in-season estimation of cabbage yields with different cultivars between years.