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Potato is a typical shallow-root crop and a good understanding of its root distribution is critical in potato water management, which is especially important for potato production in areas such as Inner Mongolia where water resource is limited. We conducted field experiments to investigate the dynamic distribution of potato root in the soil, then used this as a basis to design a new irrigation method for potato production in the region; effects of the improved irrigation and conventional method were analyzed as well by measuring plant growth, yield, water productivity and nitrogen use efficiency. The results showed that large difference existed in root distribution of potato plants at different growth stages in Inner Mongolia, suggesting the present water management strategy of uniform irrigation during potato production is inefficient and has great potential for improvement. Adjusting irrigation amount so that the wet soil zone depth covered 80% of root distribution during potato growth significantly increased water productivity, decreased N leaching, improved N absorption, nitrogen use efficiency and plant growth, leading to increased tuber yield. Therefore, the improved water management method can be used extensively. [ABSTRACT FROM AUTHOR]

Soil salinization is a widespread environmental hazard and a major abiotic constraint affecting global food production and threatening food security. Salt-affected cropland is widely distributed in China, and the problem of salinization in the Hetao Irrigation District (HID) in the Inner Mongolia Autonomous Region is particularly prominent. The salt-affected soil in Inner Mongolia is 1.75 million hectares, accounting for 14.8% of the total land. Therefore, mapping saline cropland in the irrigation district of Inner Mongolia could evaluate the impacts of cropland soil salinization on the environment and food security. This study hypothesized that a reasonably accurate regional map of salt-affected cropland would result from a ground sampling approach based on PlanetScope images and the methodology developed by Sentinel multi-sensor images employing the machine learning algorithm in the cloud computing platform. Thus, a model was developed to create the salt-affected cropland map of HID in 2021 based on the modified cropland base map, valid saline and non-saline samples through consistency testing, and various spectral parameters, such as reflectance bands, published salinity indices, vegetation indices, and texture information. Additionally, multi-sensor data of Sentinel from dry and wet seasons were used to determine the best solution for mapping saline cropland. The results imply that combining the Sentinel-1 and Sentinel-2 data could map the soil salinity in HID during the dry season with reasonable accuracy and close to real time. Then, the indicators derived from the confusion matrix were used to validate the established model. As a result, the combined dataset, which included reflectance bands, spectral indices, vertical transmit–vertical receive (VV) and vertical transmit–horizontal receive (VH) polarization, and texture information, outperformed the highest overall accuracy at 0.8938, while the F1 scores for saline cropland and non-saline cropland are 0.8687 and 0.9109, respectively. According to the analyses conducted for this study, salt-affected cropland can be detected more accurately during the dry season by using just Sentinel images from March to April. The findings of this study provide a clear explanation of the efficiency and standardization of salt-affected cropland mapping in arid and semi-arid regions, with significant potential for applicability outside the current study area. [ABSTRACT FROM AUTHOR]