Evaluation of In-Season Management Zones from High-Resolution Soil and Plant Sensors
Martina Corti,
Pietro Marino Gallina,
Daniele Cavalli,
Bianca Ortuani,
Giovanni Cabassi,
Gabriele Cola,
Antonio Vigoni,
Luigi Degano,
Simone Bregaglio
Affiliations
Martina Corti
Department of Agricultural and Environmental Sciences-Production, Landscape, Agroenergy, Università degli Studi di Milano, via Celoria 2, 20133 Milano, Italy
Pietro Marino Gallina
Department of Agricultural and Environmental Sciences-Production, Landscape, Agroenergy, Università degli Studi di Milano, via Celoria 2, 20133 Milano, Italy
Daniele Cavalli
Department of Agricultural and Environmental Sciences-Production, Landscape, Agroenergy, Università degli Studi di Milano, via Celoria 2, 20133 Milano, Italy
Bianca Ortuani
Department of Agricultural and Environmental Sciences-Production, Landscape, Agroenergy, Università degli Studi di Milano, via Celoria 2, 20133 Milano, Italy
Giovanni Cabassi
CREA-Council for Agricultural Research and Economics, Research Centre for Animal Production and Aquaculture, via Antonio Lombardo 11, 26900 Lodi, Italy
Gabriele Cola
Department of Agricultural and Environmental Sciences-Production, Landscape, Agroenergy, Università degli Studi di Milano, via Celoria 2, 20133 Milano, Italy
Antonio Vigoni
Sport Turf Consulting-Servizi per l’Agricoltura con Aeromobili a Pilotaggio Remoto, Via Cesare Battisti, 19, 20027 Rescaldina, MI, Italy
Luigi Degano
CREA-Council for Agricultural Research and Economics, Research Centre for Animal Production and Aquaculture, via Antonio Lombardo 11, 26900 Lodi, Italy
Simone Bregaglio
CREA-Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, via di Corticella 133, 40128 Bologna, Italy
The adoption of precision agriculture has the potential to increase the environmental sustainability of cropping systems as well as farmers’ income. Farmers in transition to precision agriculture need low-input and effective protocols to delineate homogenous management zones to optimize their actions without past knowledge e.g., yield maps. Different approaches have been developed so far, based on the analysis of the within-field variability in crop and soil properties, but procedures were rarely suited for operational conditions. We identified here a low-inputs protocol to map management zones from soil electrical conductivity and/or crop vegetation indices, using a winter wheat field in northern Italy as a pilot case. The reliability of the alternative data sources was evaluated at three crop development stages using a yield map as reference. Red-edge and NIR (NDRE) bands were the most reliable data sources for management zones identification, with 62%, 68%, and 74% of correct classifications at early tillering, stem elongation, and late booting, respectively. Our work identifies a minimum dataset for accurate management zones’ definition and highlights that in-season monitoring based on the red-edge band was able to reliably identify management zones already at early tillering, despite minor differences in crop growth.
