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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. [ABSTRACT FROM AUTHOR]

Potassium (K) is a primary macronutrient for overall plant growth, yield potential, product quality and stress resistance of crops. Potato (Solanum tuberosum L.) crops require a high amount of potassium to achieve the ideal yield and quality. Therefore, the determination of optimum K rate and efficient source for potato is necessary because K affects crop physiological processes, dry matter production, cooking, and processing requirements. Through modeling on the pooled data extracted from 62 studies, the highest tuber yields might be obtained at an exchangeable soil K level of 200 mg kg−1 approximately, dependent on soil pH, texture, and organic matter. Through modeling on the data of 48 studies, it also revealed that application of potassium sulfate (K2SO4) and potassium chloride (KCl) at rates of 200 kg ha−1 and potassium nitrate (KNO3) at a rate of 100 kg ha−1 might achieve the ideal yield, implying the importance of K sources in potato production. However, these values (either soil exchangeable K content, or fertilizer rates) might not be applicable in a specific growing environment for a specific potato variety. It seems that there is no discrimination among split, pre-plant or in-season application of K, although pre-plant fertilization might be a trustworthy strategy for economic tuber yield. Owing to the luxury consumption of K by potato crop, a combination of factors, including soil exchangeable K level, petiole K concentration, crop removal amount, soil conditions, management practices, climatic conditions, and potato variety, should be considered in order to make rational K fertilizer recommendations. [ABSTRACT FROM AUTHOR]

Commercial sweet corn (Zea mays convar. saccharata var. rugosa) production has a proportionally high potential for nutrient loss to waterways, due to its high nitrogen (N) requirements and low N use efficiency. Cover crops planted after sweet corn can help ameliorate N lost from the field, but farmers are reluctant to utilize cover crops due to a lack of economic incentive. Pennycress (Thlaspi arvense L.) is a winter annual that can provide both economic and environmental benefits. Five N-rates (0, 65, 135, 135 split and 200) were applied pre-plant to sweet corn. After the sweet corn harvest, pennycress was planted into the sweet corn residue with two seeding methods and harvested for seed the following spring. Residual inorganic soil N (Nmin), pennycress biomass, biomass N and yield were measured. The nitrogen rate and seeding method had no effect on pennycress yield, biomass, or biomass N content. The nitrogen rate positively affected Nmin at pennycress seeding, wherein 200N plots had 38–80% higher Nmin than 0N plots, but had no effect on Nmin at pennycress harvest. Control treatments without pennycress had an average of 27–42% greater Nmin. In conclusion, pennycress can act as an effective N catch crop, and produce an adequate seed yield after sweet corn without the need for supplemental fertilization. [ABSTRACT FROM AUTHOR]