Continuous cropping of the same crop leads to soil degradation and a decline in crop production, and these impacts could be mitigated through rotation cropping. Although crop rotation enhances soil fertility, microbial community diversity, and potato yield, its effects on the soil ecosystem multifunctionality (EMF) remain unclear. In the present research, we comparatively examined the effects of potato continuous cropping (PP) and rotation cropping [potato–oat rotation (PO) and potato–forage maize rotation (PFM)] on the soil EMF as well as the roles of keystone taxa, microbes abundance, and chemical properties in EMF improvement. It was demonstrated that soil EMF is increased in rotation cropping (PO and PFM) than PP. Soil pH was higher in rotation cropping (PO and PFM) than in PP, while total phosphorus (TP) and available phosphorus (AP) were significantly decreased than that in PP. Rotation cropping (PO and PFM) markedly changed the bacterial and fungal community compositions, and improved the potential plant-beneficial fungi, e.g., Schizothecium and Chaetomium, while reducing the abundances of the potentially phytopathogenic fungi, e.g., Alternaria, Fusarium, Verticillium dahiae, Gibberella, Plectosphaerella, Colletotrichum, Phoma, and Lectera in comparison with PP. Also, co-occurrence patterns for bacteria and fungi were impacted by crop rotation, and keystone taxa, e.g., Nitrospira.1, Lysinibacillus, Microlunatus.1, Sphingomonas.3, Bryobacter.1, Micromonospora, and Schizothecium, were enriched in PO and PFM than PP. The structural equation model (SEM) further demonstrated that cropping systems increased soil ecosystem multifunctionality through regulating SOM and keystone taxa (Schizothecium1), and keystone taxa were mediated by soil pH. This study suggested that rotation cropping might contribute to the improvement of soil ecosystem multifunctionality as well as the development of disease-suppressive soils in comparison with potato continuous cropping.

With the development of sustainable agriculture, trials on the benefits of the application of organic fertilizers around the world have been conducted. Herein, we investigated the impact of the pure chemical fertilizers (CFs) combined with organic fertilizers compared with the application of CFs (100% CFs) and no fertilizers (NFs) on soil properties as well as the yield and quality of Chinese cabbage through meta-analysis. Results indicate that: (1) Compared with NFs, the application of organic fertilizers can significantly improve the yield and quality of Chinese cabbage and increase soil nutrients. (2) Compared with CFs, the application of organic fertilizers can increase the fresh weight, number of leaves, transverse diameter, leaf length, and development of Chinese cabbage per plant, with increases of 8.54%, 6.6%, 9.905%, 8.42%, and 10.03%; Meanwhile, organic fertilizers can significantly increase the yield (total amount of above-ground parts produced) and commercial yield (the portion that meets the required quality standards and is intended for sale) of Chinese cabbage to increase the yield and commercial yield by 10.08% and 35.56%, respectively. However, it has no significant impact on the income from growing Chinese cabbage. (3) Compared with CFs, the application of organic fertilizers can significantly increase the content of vitamin C (11.06%), soluble sugar (19.16%), and soluble protein (8.83%) and reduce the content of nitrate and nitrite in Chinese cabbage, with a reduction of up to 19.02% and 20.9%, respectively. The application of organic fertilizers will also have a certain impact on the absorption of heavy metals in Chinese cabbage. (4) Compared with CFs, the application of organic fertilizers can significantly improve soil organic matter, soil carbon sequestration, nitrogen absorption, and potassium absorption, showing increases of 12.73%, 13.19%, 7.91%, and 7.37%, and the application of organic fertilizers reduces soil electrical conductivity and available nitrogen, showing decreases of 36.78% and 38.75%, respectively. (5) The application of organic fertilizers significantly increased the content of soil urease and soil sucrase, increasing by 9.42% and 17.16%, respectively. This study helps inform the application of organic fertilizers in Chinese cabbage production.

In-season nitrogen (N) status diagnosis is an effective way to guide split N applications for improved profitability and minimized negative environmental impacts. Petiole nitrate-N concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) N status diagnosis but is limited because of destructive sampling and chemical processing needs. Leaf sensors can be used to predict PNNC and other N status indicators and overcome these challenges. The SPAD meter is a sensor commonly used to estimate leaf chlorophyll (Chl) based on transmittance, while Dualex is a newer leaf sensor that can also measure leaf flavanol (Flav) and anthocyanin (Anth) through Chl fluorescence. Limited research has been conducted to compare the two leaf sensors for potato N status assessment, despite their respective success in N status diagnosis for other crops. Therefore, the objectives of this study were to 1) compare the performance of the Dualex sensor relative to the SPAD meter for predicting potato N status indicators when only sensor data are used, 2) evaluate the potential of improving potato N status prediction using multi-source data fusion compared with only using leaf sensor data, and 3) develop practical strategies for leaf-sensor-based in-season potato N status diagnosis. The plot-scale experiments were conducted in Becker, Minnesota, USA in 2018, 2019, 2021, and 2023 involving different cultivars, N treatments, and irrigation treatments in a split plot design with three replications. Leaf sensor data and plant samples were simultaneously collected and processed multiple times at key growth stages each year. Daily weather data were also collected at the on-site weather station. Different in-season potato N status indicators including PNNC and N nutrition index (NNI) were derived from plant samples, while weather- and management-related parameters were calculated using the weather data and management records. Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical in-season potato N status diagnostic strategy was developed using linear support vector regression model with SPAD, cultivar information, accumulated growing degree days (GDDs), accumulated total moisture, and as-applied N rate to predict vine or whole plant NNI, achieving an R² of 0.80 - 0.82, accuracy of 0.75 - 0.77, and a Kappa statistic of 0.57 - 0.58 (near-substantial). Further research is also required to determine the critical N dilution curve and sufficiency ranges of NNI for potatoes based on different genetic, environmental, and management conditions to better support decision-making.