Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.

Climate change is expected to influence crop growth through frequent drought and heat extremes, and thus, drought and heat tolerance are of increasing importance as major breeding goals for cereal crops in Central Europe. Plant physiological water status traits are suitable for phenotyping plant drought/heat tolerance. The objective of this study was to determine whether relative leaf water content (RLWC), plant canopy temperature (CT), and carbon isotope discrimination (CID) are suitable for phenotyping the drought/heat resistance of German winter wheat for future climate resilience. Therefore, a comprehensive field evaluation was conducted under drier and warmer conditions in Moldova using a space-for-time approach for twenty winter wheat varieties from Germany and compared to twenty regionally adapted varieties from Eastern Europe. Among the physiological traits RLWC, CT, and CID, the heritability of RLWC showed the lowest values regardless of year or variety origin, and there was no significant correlation between RLWC and grain yield regardless of the year, suggesting that RLWC did not seem to be a useful trait for distinguishing origins or varieties under continental field conditions. Although the heritability of CT demonstrated high values, the results showed surprisingly low and nonsignificant correlations between CT and grain yield; this may have been due to a confounding effect of increased soil temperature in the investigated dark Chernozem soil. In contrast, the heritability of CID in leaves and grain was high, and there were significant correlations between grain yield and CID, suggesting that CID is a reliable indirect physiological trait for phenotyping drought/heat resistance for future climate resilience in German wheat.

Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel approach for large-scale N monitoring. In this study, we evaluated the performance of different types of spectral bands and indices (SIs) coupled with ensemble learning models (ELMs) at retrieving the plant N concentration (PNC) and plant aboveground biomass (AGB) of potato from Sentinel-2 images. Cloud-free Sentinel-2 imagery was acquired during the tuber-formation to starch-accumulation stages from 2020 to 2021. Fourteen optimal SIs were selected using the successive projections algorithm (SPA) and principal component analysis (PCA). The PNC and AGB estimation models were then built using an ELMs. The results showed that the SIs based on chlorophyll absorption bands were strongly related to potato PNC and AGB. Also, the N-correlated bands were mainly concentrated in the red-edge (705 nm) and short-wave infrared (1610 and 2190 nm) regions. The ELMs successfully predicted PNC and AGB (R²_PNC = 0.74; R²_AGB = 0.82). Compared with the other five base models (k-nearest neighbor (KNN), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR)), the ELMs provided higher PNC and AGB estimation accuracy and effectively reduced overfitting to training data. This study demonstrated that the promising solution of using SPA-PCA coupled with an ensemble learning model improves the estimation accuracy of potato PNC and AGB based on Sentinel-2 imagery data.

Improvement of nitrogen (N) use efficiency is urgently needed since excessive application of N fertilizer has been widespread in small-scale fields in China, causing great losses of N fertilizer and environmental pollution. In the present study, a simple technology, termed the Green Window Approach (GWA), to optimize N strategies for cereal crops is presented. The GWA represents an on-field demonstration site visualizing the effects of incremental N levels and enables farmers to conduct such a trial within their own fields. The lowest N rate that achieves no visible change in plant growth or biomass shows the optimal N requirement of crops. Therefore the objective was to develop the key procedures of GWA and to evaluate the effects of its application in cereal crops on grain yield, N use efficiency and economic benefit. A total of seven GWA trials were performed from 2009 to 2011 on farmers’ irrigated wheat fields in the North China Plain. The GWA consisted of eight small plots placed in a compact layout on a well-accessible part of the field. Plot size varied from 2·5×2·5 to 4×4 m2, depending on the size and shape of each field. All GWA plots received basal nitrogen (N), phosphorus (P) and potassium (K) rates of 30 kg N/ha (except for the nil-N plot), 80 kg P2O5/ha and 100 kg K2O/ha. Nitrogen supplies, including residual soil nitrate in 0–90 cm determined at Zadoks growth stages (GS) 21–23 in early spring and the split-topdressing N at GS 21–23 and GS 41–52, were incrementally increased from 0 to 420 kg N/ha. The remaining part of the field still received farmers’ customary fertilization (FCF). Optimal N rate could be estimated as the lowest N rate that achieved no visible change in plant growth at GS 60–73. Compared with FCF area, grain yield was increased by 13% to a maximum or near maximum value of 5·8 t/ha, optimal N rate was sharply decreased by 69% to 116 kg N/ha, apparent N recovery was greatly increased from 11 to 46%, whereas the cost of fertilizer input was decreased by 57% to 1045 Chinese Renminbi (RMB)/ha (162 US$/ha). Most importantly, the GWA does not need laboratory facilities, complicated procedures or professional knowledge of N balances, and farmers can easily understand and use GWA by themselves.

Anthropogenic ammonia (NH₃) emissions, of which about 95 % are from agriculture, have led to environmental pollution, resulting in tremendous damage to human health and ecosystems. Thus, the NEC Directive 2016/2284/EU sets national reduction targets for NH₃ emissions in individual EU countries. To implement the NEC Directive for NH₃ emission targets, Germany amended the Fertilizer Application Ordinance in 2017 and 2020 (DüV_amended) and set the air pollution control regulation, Technical Instructions on Air Quality Control (TA_Luft). This study aimed to evaluate the impact of the DüV_amended on NH₃ mitigation from applying livestock manure, digestates, synthetic nitrogen (N) fertilizers, and TA_Luft on housing and storage. This study showed that Germany reached the first national NH₃ reduction target in 2020, as set by the NEC directive. The German DüV_amended, a significant policy change, has profoundly impacted NH₃ emission mitigation from agriculture after 2017 by implementing measures aimed directly at NH₃ reduction, reducing N surpluses, and improving N use efficiency. The reduction in NH₃ emissions from synthetic N fertilizers between 2016 and 2022 contributed about 51 % to the decrease from the agricultural sector over the same period. Among the synthetic fertilizers, NH₃ reduction from urea between 2016 and 2022 accounted for around 83 % of the total reduction from synthetic N, indicating that the NH₃ emissions from urea fertilizer by reducing urea application and mandating urea to be incorporated immediately or to be stabilized with urease inhibitors played a crucial role in the sharp decrease in NH₃ emissions over the last years in Germany. Achieving a high yield by lowering the synthetic N rate in this study strongly suggests that optimal reduction in N rate does not necessarily result in yield losses but rather in a pivotal relationship between the agronomic and environmental performance and indicates that the DüV_amended was an effective measure that can reduce the NH₃ emissions. Over 80 % of Germany's annual agricultural NH₃ emissions in 2021 and 2022 originated from livestock and digestates from energy crops. Mandatory close to the soil band application of slurry and digestates on cultivated cropland since 2020 reduced NH₃ emissions. In addition, banning of broadcast application of slurry to grassland and manure incorporation within one hour on uncultivated soils will become mandatory in 2025 to comply with NEC 2030´s target of 29 % NH₃ reduction relative to 2005. The recent German air pollution control regulation (TA_Luft) enforces abatement measures such as air purifiers in large poultry and pig housings and covered storage of slurry and digestate storages of large farms. The results of the German NH₃ abatement strategy for synthetic N fertilizers may help reduce NH₃ emissions worldwide, especially for countries consuming high amounts of urea fertilizers.