| Titre : | Dynamic management zones for irrigation scheduling (2020) |
| Auteurs : | M. Fontanet ; M. Fontanet ; E. Scudiero ; T.H. Skaggs ; D. Fernàndez-Garcia ; F. Ferrer ; G. Rodrigo ; J. Bellvert |
| Type de document : | Article |
| Dans : | Agricultural Water Management (vol. 238, 1 August 2020) |
| Article en page(s) : | p. 1-12 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Thésaurus IAMM AGRICULTURE DE PRECISION ; TELEDETECTION ; AIDE A LA DECISION ; AGRICULTURE ; PLANIFICATION ; CULTURE IRRIGUEE ; APPROVISIONNEMENT EN EAU |
| Résumé : | Irrigation scheduling decision-support tools can improve water use efficiency by matching irrigation recommendations to prevailing soil and crop conditions within a season. Yet, little research is available on how to support real-time precision irrigation that varies within-season in both time and space. We investigate the integration of remotely sensed NDVI time-series, soil moisture sensor measurements, and root zone simulation forecasts for in-season delineation of dynamic management zones (MZ) and for a variable rate irrigation scheduling in order to improve irrigation scheduling and crop performance. Delineation of MZ was conducted in a 5.8-ha maize field during 2018 using Sentinel-2 NDVI time-series and an unsupervised classification. The number and spatial extent of MZs changed through the growing season. A network of soil moisture sensors was used to interpret spatiotemporal changes of the NDVI. Soil water content was a significant contributor to changes in crop vigor across MZs through the growing season. Real-time cluster validity function analysis provided in-season evaluation of the MZ design. For example, the total within-MZ daily soil moisture relative variance decreased from 85% (early vegetative stages) to below 25% (late reproductive stages). Finally, using the Hydrus-1D model, a workflow for in-season optimization of irrigation scheduling and water delivery management was tested. Data simulations indicated that crop transpiration could be optimized while reducing water applications between 11 and 28.5% across the dynamic MZs. The proposed integration of spatiotemporal crop and soil moisture data can be used to support management decisions to effectively control outputs of crop * environment * management interactions. |
| Cote : | Réservé lecteur CIHEAM |
| URL / DOI : | https://doi.org/10.1016/j.agwat.2020.106207 |
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