Decision Support From On-Field Precision Experiments

Empirically driven adaptive management in large-scale commodity crop production has become possible with spatially controlled application and sub-field scale crop monitoring technology. Site-specific experimentation is fundamental to an agroecosystem adaptive management (AAM) framework that results in information for growers to make informed decisions about their practices with their local data. Crop production and quality response data from combine harvester mounted sensors and internet-available remote sensing allows spatial variability assessment of the experimentally applied input as well as the impact of environment and management covariates that also spatially vary across the field. Repeating the experiments and gathering year-specific economic and weather data allows for incorporating temporal variability into simulation models driven by the locally parameterized crop response models. Field-specific simulation allows comparison of input management alternatives to identify practices that provide the greatest profitability, minimization of pollution from the inputs, and economic resilience. Communication of information to farmers through carefully designed decision support will determine if precision agriculture (PA) provides transparent interactive algorithms that empower farmers to become more knowledgeable agroecologists, or becomes the end point of industrialized agriculture that removes human decisions from agriculture.