Data and monitoring needs for a more ecological agriculture

Abstract

Information on the life-cycle environmental impacts of agricultural production is often limited. As demands grow for increasing agricultural output while reducing its negative environmental impacts, both existing and novel data sources can be leveraged to provide more information to producers, consumers, scientists and policy makers. We review the components and organization of an agroecological sensor web that integrates remote sensing technologies and in situ sensors with models in order to provide decision makers with effective management options at useful spatial and temporal scales for making more informed decisions about agricultural productivity while reducing environmental burdens. Several components of the system are already in place, but by increasing the extent and accessibility of information, decision makers will have the opportunity to enhance food security and environmental quality. Potential roadblocks to implementation include farmer acceptance, data transparency and technology deployment.

Solutions for a cultivated planet

“…We need better data and decision support tools to improve management decisions 73 , productivity and environmental stewardship. (4) The search for agricultural solutions should remain technologyneutral.…”

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

“…Due to the rapid rate of change in crop phenology and progress-beneath the weekly time step [5,17]-reasonably cloud-free imagery is generally required with greater frequency for agricultural monitoring than it is for applications that monitor more static phenomena or processes [18]. For crop yield and crop condition, for example, clear views are needed roughly weekly or at least biweekly, although even more frequent data are valuable [18][19][20][21][22].…”

“…For crop yield and crop condition, for example, clear views are needed roughly weekly or at least biweekly, although even more frequent data are valuable [18][19][20][21][22]. Due to this requirement for frequently sampled data, global cropland monitoring to date has been predominately undertaken with coarse spatial resolution data (defined in the context of GEOGLAM as greater than 100 m) [23,24], with near-daily MODIS-class observations at 250-500 m and with broad spectral coverage providing the primary data source over the past decade [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

“…Unfortunately, remote sensing can only rarely measure any of these dimensions directly and therefore a combination of remote sensing and ground-based data is often needed to determine land use intensity metrics. This is problematic because comprehensive and detailed in-situ data on land management is unavailable for most parts of the world, either because of the lack of monitoring schemes or the confidentiality issues [15,26,27]. Approaches that allow to better characterize management intensity directly from satellite imagery are therefore potentially highly beneficial for a better understanding of the impacts of agriculture on the environment.…”