Data di Pubblicazione:
2019
Abstract:
Nitrogen (N) fertilisation determines maize grain yield (MGY). Precision agriculture (PA) allows matching crop Nrequirements in both space and time. Two approaches have been suggested for precision N management, i.e. management zones (MZ) delineation and crop remote and proximal sensing (PS). Several studies have demonstrated separately the advantages of these approaches for precision N application. This study evaluated their con-venient integration, considering the influence of different PA techniques on MGY, N use efficiency (NUE), and farmer's net return, then providing a practical tool for choosing the fertilisation strategy that best applies in each agro-environment. A multi-site-year experiment was conducted between 2014 and 2016 in Colorado, USA. The trial compared four N management practices: uniform N rate, variable N rate based on MZ (VR-MZ),variable N rate based on PS (VR-PS), and variable N rate based on both PS and MZ (VR-PSMZ), based on their effect on MGY, partial factor productivity (PFPN), and net return above N fertiliser cost (RANC). Maize grain yield and PFPN maximisation conflicted in several situations. Hence, a compromise between obtaining high yield and increasing NUE is needed to enhance the overall sustainability of maize cropping systems. Maximisation of RANC allowed defining the best N fertilisation practice in terms of profitability. The spatial range in MGY is a practical tool for identifying the best N management practice. Uniform N supply was suitable where no spatial pattern was detected. If a high spatial range (N100 m) existed, VR-MZ was the best approach. Conversely, VR-PS per-formed better when a shorter spatial range (b16 m) was detected, and when maximum variability in crop vigour was observed across the field (range of variation = 0.597) leading to a larger difference in MGY (range of variation = 13.9 Mg ha−1). Results indicated that VR-PSMZ can further improve maize fertilisation for intermediate spatial structures (43 m).
Tipologia CRIS:
03A-Articolo su Rivista
Keywords:
Data fusion; Management zones; Precision fertilisation; Proximal crop sensing; Variable rate N application
Elenco autori:
Cordero E.; Longchamps L.; Khosla R.; Sacco D.
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