|FONTANET, MIREIRA - Labferrer|
|SCUDIERO, ELIA - University Of California - Cooperative Extension Service|
|FERNÀNDEZ-GARCIA, DANIEL - Universitat Politècnica De Catalunya (UPC)|
|FERRER, FRANCESCA - Labferrer|
|RODRIGO, GEMA - Labferrer|
|BELLVERT, JOAQUIM - Institute Of Agrifood Research And Technology|
Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/11/2020
Publication Date: 5/7/2020
Citation: Fontanet, M., Scudiero, E., Skaggs, T.H., Fernàndez-Garcia, D., Ferrer, F., Rodrigo, G., Bellvert, J. 2020. Dynamic management zones for irrigation scheduling. Agricultural Water Management. 238. https://doi.org/10.1016/j.agwat.2020.106207.
Interpretive Summary: Precision agriculture can improve water use efficiency by matching irrigation recommendations to prevailing soil and crop conditions in a field. 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 vegetation index time-series, soil moisture sensor measurements, and root zone simulation forecasts for in-season delineation of dynamic management zones (MZ) and variable rate irrigation scheduling. The results indicate that our proposed approach is a viable method for irrigation management that has the potential for conserving water and maximizing crop yields. The research will be of interest to scientists, engineers, irrigation consultants, and growers interested in integrating new technologies to improve agricultural water use efficiency.
Technical Abstract: 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.