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Delineating management zones (MZs) is considered one of the most important steps towards precision nitrogen (N) management, as MZs are required to optimize N inputs and improve environmental health. However, no reports have fully explored the optimization of regional MZs related to policymaking to achieve long-term Sustainable Development Goals. This study developed a new sustainable environmental index (SEI) by integrating the Euclidean distance after feature normalization, spatial autocorrelation, and expert knowledge. The SEI was then used to delineate MZs in the main wheat-producing provinces of China using the fuzzy C-mean clustering. The results showed that compared to the two data-driven-based methods (Random Forest- and all variables-based methods), the SEI-based method performed the best and identified 9 MZs in terms of practical production and spatial distribution of zones. Further analysis indicated that the dominant drivers of MZ delineation showed strong spatial heterogeneity and high input uncertainty. Climatic factors explained 15.6% of the yield variability, while both soil factors and topographic factors individually accounted for 10.2% of the variability. The similar spatial characteristics of input uncertainty were found to be consistent across various MZs, with a high level of uncertainty ranging from 0.7 on a scale of 0 to 1. Finally, this study provided potentially valuable suggestions for policymakers and farmers, as well as possible directions for further N management. Overall, the proposed methodological framework on regional MZs has important implications for precision N management, providing a new perspective for intensive sustainable development. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "A novel method for optimizing regional-scale management zones based on a sustainable environmental index" published in the journal Precision Agriculture. The original version of the article contained a mistake in the published version, specifically two circles above Figure 4(k) that appeared mistakenly in the PDF version. However, the online version of the article is correct. The correction has been made and the original article has been updated. The correction notice is provided by the authors and includes the names of all the authors involved in the article. Springer Nature, the publisher, remains neutral with regard to jurisdictional claims and institutional affiliations. [Extracted from the article]

Nitrogen (N) fertilizer application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions to the atmosphere. The objectives of this study were to: (i) to demonstrate that properly identified variable rates of N fertilizer lead to higher use efficiency and (ii) to evaluate the capability of high spectral resolution satellite to detect within-field crop N response using vegetation indices. This study evaluated three N fertilizer rates (30, 70, and 90 kg N ha) and their response on durum wheat yield across the field. Fertilizer rates were identified through the adoption of the SALUS crop model, in addition to a spatial and temporal analysis of observed wheat grain yield maps. Hand-held and high spectral resolution satellite remote sensing data were collected before and after a spring side dress fertilizer application with FieldSpec, HandHeld Pro and RapidEye™, respectively. Twenty-four vegetation indices were compared to evaluate yield performance. Stable zones within the field were defined by analyzing the spatial stability of crop yield of the previous 5 years (Basso et al. in Eur J Agron 51: 5, ). The canopy chlorophyll content index (CCCI) discriminated crop N response with an overall accuracy of 71 %, which allowed assessment of the efficiency of the second N application in a spatial context across each management zone. The CCCI derived from remotely sensed images acquired before and after N fertilization proved useful in understanding the spatial response of crops to N fertilization. Spectral data collected with a handheld radiometer on 100 grid points were used to validate spectral data from remote sensing images in the same locations and to verify the efficacy of the correction algorithms of the raw data. This procedure was presented to demonstrate the accuracy of the satellite data when compared to the handheld data. Variable rate N increased nitrogen use efficiency with differences that can have significant implication to the NO emissions, nitrate leaching, and farmer's profit. [ABSTRACT FROM AUTHOR]