Consumer demand for organically grown produce has increased dramatically over the past decade, most likely because of the perceived benefits to the environment and human health. A major component of organic production is providing organic sources of nutrients to promote plant growth as well as sustain soil quality. Organic nutrition of plants can present opportunities and challenges to the grower. The primary objective of this article is to review scientifically based information dealing with the effects of organic nutrient sources on crop yields and quality, soil properties, and environmental risks. Effects of organic nutrient sources are often evaluated by comparison with conventional production, but this approach can be problematic because nutrient source may be confounded with many other cropping system components. Despite these drawbacks, a careful examination of the literature suggests the following conclusions. Soil quality is generally improved with application of organic nutrient sources, but careful management is required to avoid environmental risks of nitrate (NO ₃ ) leaching and phosphorus accumulation. Provided that nutrient supply is equal, yields with organic sources tend to be similar to those with inorganic sources. However, lack of available nitrogen (N) that is synchronous with plant demand often limits yields in organic cropping systems. Limited N availability and varied supply of other nutrients from organic sources may contribute to the differences sometimes observed in dry matter content, tissue NO ₃ and mineral concentration, vitamin C and other phytochemicals, and taste. Phytonutrient content also may be affected by differences in pest control strategies among cropping systems regardless of nutrient source. There is a slight, but significantly, increased risk of produce contamination by Escherichia coli and other enteric bacteria contamination on produce when organic nutrient sources are used, but if proper guidelines are followed, contamination with the lethal serotype O157:H7 does not appear to be a major concern. Appropriate management of organic inputs is critical to achieving potential benefits for crop production and soil quality.

Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R² = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R² = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.