It is worth mentioning that at all the three tiers of sites, cross-scale sensing technology should be able to provide already rich remote-sensing based observations, which should provide the necessary model inputs and model constraints for MDF. Tier 1 – Super sites: This tier includes sites that have collected a complete suite of measurements data that can be regarded as gold standard datasets . An ideal super site should include measurements that range from biogeophysics to biogeochemistry , i.e. a dataset that is sufficient to recreate the soil-plant-atmosphere continuum, and evaluate/benchmark the major ecosystem processes simulated by models. Thus a typical super site should at least include eddy-covariance flux tower, extensive and deep soil samples, ground-level remote sensing, and various other advanced measurements . Existing examples of research infrastructure that already supports many of these “gold-standard” data variables include the USDA Long-Term Agroecosystem Research network, some National Ecological Observatory Network sites, and AmeriFlux sites on cropland and pasture land . Further, the recently launched U.S. Department of Energy ARPA-E SMARTFARM sites have been collecting soil, crop, and GHG fluxes data with even greater spatial and temporal resolutions , enabling a new generation of R&D development such as high-resolution remote sensing monitoring, or novel modeling methods that can capture granular dynamics such as hot-spot and hot-moment patterns of GHG emissions. Tier 1 super sites would enable detailed model calibration and out of-sample validation by virtue of the fact that gold-standard datasets capture whole ecosystem flux , soil carbon flux and stock, plant biomass etc. What would make the Tier 1 super sites more useful is to add paired experiments with detailed measurements for the pairs.
For example,vertical gardening in greenhouse setting up two neighboring sites with one growing cover crop and the other not, and keeping other management practices the same or similar enough, the difference of measurements could provide strong scientific evidences and thus validation data for quantifying the carbon outcome of different management practices. Successful examples of paired experiments with eddy-covariance flux measurements have been demonstrated in rice methane emission using alternate wetting and drying . Super sites also provide further validation for the cross-scale sensed E, M, C variables. Tier 2 – Intermediate sites: This tier includes an extensive number of sites that only have a few key ground measurements but do not have a complete suite of observations as the Tier 1 super sites. Using these ground measurements and also remotely sensed observations, MDF can be conducted, and validation can still be made directly to compare the simulated crop yield, SOC stock and SOC changes with ground observations. When doing model validation at the Tier 2 sites, only basic information about site location and management history will be provided, and the modeling team should report their simulation results for independent comparison with observations. Tier 3 – Scaling sites: This tier includes virtually any site or field that requires carbon outcome quantification. Little or no ground measurements are available at these sites. This tier of sites thus represents the real-world situation for operational use. However, using the cross-scale sensing technologies , all random fields will still have a suite of remotely sensed E, M, C data available to enable MDF and quantify both carbon outcomes and associated uncertainty at all these fields. Model verification at every field is also made possible when extra remotely sensed observations can be used as independent validation data. It is worth noting that Tier 3 almost entirely relies on remotely sensed and/or public-database E, M, C information, which highlights the importance of cross-scale sensing to enable such a new MDF approach. Looking forward, the “System-of-Systems” solution will be the most promising technology for field-level carbon outcome quantification.
One of the biggest advantages of the “System-of-Systems” solution is that it is an inclusive framework that can embrace new technology and has the potential to ingest new scientific discoveries and information, and thus can continue to evolve with the whole scientific community and technology trends. While prototypes of such a “System-of-Systems” solution are emerging for certain crop types and geography , this integrated system consists of several components that are still at their early stages, thus requiring considerable R&D investment by government and industry. Coincidentally, these investments will build the foundation for the next generation of precision agriculture whose scope has been expanded from increasing productivity and efficiency with site specific management , to the integration of sensing, big-data analytics and automation for guiding sustainable farming . However, technical advances alone are insufficient for substantiating the agricultural carbon market or agricultural sustainability more broadly; success will also rely on synergies among citizens, researchers, corporations, NGOs and governments to remove scientific and practical hurdles. First and foremost, we should fully acknowledge that agricultural carbon outcomes are deeply rooted in complex agroecosystems, and a holistic system view of carbon, nutrient, energy, and water cycles strongly coupled with human management should be the guiding principle. Above ground and below ground processes of carbon cycle collectively determine the SOC change , thus only focusing on changes in soil carbon pools while neglecting other critical carbon processes may lead to limited success. The tight connection of carbon cycle with other biogeochemical cycles and water cycle also highlights the importance of soil moisture, soil oxygen and chemical characterization of litter, which links SOC with the GHG emissions . Many unknowns about these above linkages exist . Coordinated research on understanding the holistic carbon nutrient-water cycles for agroecosystems is a priority that could be effectively pursued by leveraging the Integrated Model-Observation Experiment Paradigm . ModEx promotes the idea that models should be developed with the current best knowledge and corroborated with observational and experimental data, and models are then used to identify opportunities for additional field and lab-based research to fill gaps in further understanding system structure and function.
Second, we should use community efforts to develop unified protocols that guide measurements and modeling schemes to understand and reduce the uncertainty of carbon outcome quantification. Such protocols must be established through collective effort to achieve scientific rigor and transparency. Existing efforts led by certification organizations such as Verra and Climate Action Reserve are important and valued, but tend to be simplistic, conservative, and not always well-adapted to the nuances of production agriculture, given the limited empirical data and insufficient MRV tools . To successfully establish public confidence in low-carbon bio-energy feedstock, climate-smart commodities and agricultural carbon credit markets, a concerted effort of more advanced field work, data collection, and modeling assessment will be necessary. It is anticipated that debate will intensify as more disciplines and stakeholders become involved in the new phase of protocol development and validation,greenhouse vertical farming especially when the necessary rigor requires technical sophistication beyond traditional quantification approaches . To foster open and constructive conversations that increase credibility and the public confidence in carbon outcome quantification methods, three principles must be emphasized. First, the quantification uncertainty of field level carbon outcomes must be emphasized, and especially for the market-based instruments, such as climate-smart commodities and carbon credit markets, the uncertainty of the calculated carbon benefits should be reflected in climate-smart commodities’ price premium, or carbon credits pricing and policy design to ensure that the incentivized impact is not over- or under compensated. For example, the standard deviation of a MRV system can be used to discount the value of credits generated . This is an essential requirement for the protocol to be usable, not just a subjective technical preference. Second, validation is the only way to report system-wide uncertainty. No exemption should be made for any quantification tool, even if the tool is widely used or peer reviewed. There are some academic-based model intercomparison MIP efforts that can shed light on how to set up such validations, but given the transaction purpose of carbon credits, a high bar must be set for acceptable model performance. Third, demonstrating performance at the scale of an individual field is critical. Due to the challenges of achieving scalability, some practitioners suggest compromise by focusing on the aggregated accuracy of quantified carbon credit . We argue that aggregated accuracy, which is almost impossible to validate, must come from field-level accuracy. Next, establishing high-quality and comprehensive datasets and inter-comparison infrastructure for developing, calibrating, and validating MRV systems of carbon benefits is essential to building stakeholder trust in these market-based emission reduction instruments. The high-quality and comprehensive dataset to represent the three Tier validation system should ensure site representativeness to include different soil, weather, crop, and management types, and be open-source but compiled under a protocol of community-wide acceptance. An analogy is the Image Net database for computer vision and AI research, with which new algorithms will be bench marked to show their progress in visual object recognition.
Establishing an “Image Net for Agriculture” is certainly more challenging given the complexity of carbon quantification. Due to the often large uncertainty associated with agricultural measurements, protocols for standardized data collection, and processing techniques must be carefully evaluated and imposed. Some long term experiment and observation networks have collected a complete suite of E, M, C variables and have the great potential to provide high-quality and comprehensive data. Lastly, a large number of controlled experiment sites can be used to test the model scalability. These sites often have limited amounts of ground measurements but represent the real-world conditions for operational use. Further investment in high-quality data collection should prioritize experiments that can help understand the carbon outcomes associated with different bundles of carbon-outcome-related practices, such as the combination of no-till and cover crop, as well as measurements that can disentangle the opaque “black box” of complex plant-soil-microbe interactions . In addition, deep sampling of soils beyond the typical surface sampling depths is necessary to accurately quantify the extent of SOC changes and to corroborate estimates by models. Developing cyber infrastructure to ensure archiving and sharing of the scientific data is also highly important and should be an investment priority. Such cyber infrastructure development should be guided by the FAIR guiding principle for the collected scientific data management and stewardship , with a thorough consideration of privacy protection of farmer data. Finally, while our discussion has mainly focused on agricultural carbon outcomes, it is important to note the myriad environmental and economic co-benefits , which in turn can bring further benefits to carbon mitigation programs per se. Some recent case studies have demonstrated that, given the relatively low carbon credit price, participation of farmers may be primarily driven by these cobenefits . The “System-of-Systems” framework proposed in this perspective can be extended to assist the accounting of these co-benefits, and inform sustainable agroecosystem management by holistically studying the often coupled carbon, water, and nutrient cycles and human activities, a topic itself at the frontier of Earth system science. Poverty, often defined as very low socioeconomic status or lack of material wealth, negatively impacts almost every aspect of human biological functioning . It undermines growth and development, compromises basic physiological functions like immunity, intensifies disease, and worsens mental health . Lack of material wealth is a fundamental stressor in humans, both in terms of lack of access to basic needs, but also because of the low power and stigmatized social meanings attached . In studies that treat poverty as a driver of bio-cultural variation, “poverty” is most often operationalized as lack of wealth within the cash economy . Direct measures often focus on assessment of income or purchased material assets like housing materials or vehicles . Other often applied proxy measures are related to consumption or current or predicted participation in the cash economy like occupation or education . Recently, a study by Hadley, Maxfield and Hruschka , clarified that a dimension of agricultural wealth independent from cash economy wealth can show very different associations with human biological outcomes compared to those based in the cash economy. They found in a study of households in several sub-Saharan African countries that success in the cash economy was associated with increased risk of HIV infection, while success in agricultural activities often proved protective against that risk. Here we expand on the proposition that poverty measures giving primacy to the lack of success in the cash economy could overlook a crucial dimension of poverty that is important for understanding associations with well being, specifically the potential buffering role of agricultural forms of household wealth.