Abstract. A significant proportion of the global carbon emissions to the atmosphere originate from agriculture. Therefore, continuous long-term monitoring of CO2 fluxes is essential to understand the carbon dynamics and balances of different agricultural sites. Here we present results from a new eddy covariance flux measurement site located in southern Finland. We measured CO2 and H2O fluxes at this agricultural grassland site for 2 years, from May 2018 to May 2020. In particular the first summer experienced prolonged dry periods, which affected the CO2 fluxes, and substantially larger fluxes were observed in the second summer. During the dry summer, leaf area index (LAI) was notably lower than in the second summer. Water use efficiency increased with LAI in a similar manner in both years, but photosynthetic capacity per leaf area was lower during the dry summer. The annual carbon balance was calculated based on the CO2 fluxes and management measures, which included input of carbon as organic fertilizers and output as yield. The carbon balance of the field was −57 ± 10 and −86 ± 12 g C m−2 yr−1 in the first and second study years, respectively.
Abstract. A significant proportion of the global carbon emissions to the atmosphere originates from agriculture. Therefore, continuous long-term monitoring of CO2 fluxes is essential to understand the carbon dynamics and balances of different agricultural sites. Here we present results from a new eddy covariance flux measurement site located in southern Finland. We measured CO2 and H2O fluxes at this agricultural grassland site for two years from May 2018 to May 2020. Especially the first summer experienced prolonged dry periods, which affected the CO2 fluxes, and substantially larger fluxes were observed in the second summer. During the dry summer, leaf area index (LAI) was notably lower than in the second summer. Water use efficiency increased with LAI in a similar manner in both years, but photosynthetic capacity per leaf area was lower during the dry summer. The annual carbon balance was calculated based on the CO2 fluxes and management measures, which included input of carbon as organic fertilisers and output as yield. The carbon balance of the field was −50 ± 68 g C m−2 yr−1 and −118 ± 24 g C m−2 yr−1 during the first and second study year, respectively. We estimated that on average the grassland exceeded the global 4 per 1000 goal to increase the soil carbon content.
Abstract. Better monitoring, reporting, and verification (MRV) of the amount, additionality, and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies, and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, modeling, and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called the Field Observatory (v1.0, https://www.fieldobservatory.org/, last access: 3 February 2022). The Field Observatory is designed as an online service for near-real-time model–data synthesis, forecasting, and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site, which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices as well as provide an MRV tool for decision support.
Abstract. Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON’s first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support.
<p>Mitigation of climate change requires &#8211; besides reductions in greenhouse gas emissions &#8211; actions to increase carbon sinks and storages in terrestrial ecosystems. Agricultural lands have a high potential for increased carbon sequestration through climate-smart land management and agricultural practices. However, in order to make climate-smart farming an accredited solution for climate policy, carbon markets and product footprints, reliable verification of carbon sequestration is needed. Direct measurement of the changes in soil carbon stock is slow, laborious and expensive and has significant uncertainties due to large background stocks and high spatial variability. An alternative is to infer the soil carbon stock change from measurements of the gaseous carbon fluxes between ecosystems and the atmosphere using the micrometeorological eddy covariance (EC) method.</p><p>Eddy covariance measures net ecosystem exchange (NEE), which is a small difference between two large components: carbon uptake by photosynthesis and losses due to plant and soil respiration. Therefore, small changes in either of them results in a large change in NEE. This sensitivity is also reflected in uncertainty estimates, which are critical for defining confidence intervals for annual carbon budget estimates and for making statistically valid comparisons of different management practices. &#160;In addition, there are inevitable gaps in the data due to instrument failure, power shortages and non-ideal flow conditions. Therefore, in order to calculate daily and annual sums, the collected data must be temporally upscaled or gap-filled, which constitutes a major additional source of uncertainty. This study compares two different gap-filling methods for CO&#8322; fluxes: (1) an artificial neural network and (2) non-linear regression, which uses temperature and radiation as drivers. Uncertainties associated with both methods are estimated and discussed. The analysis is based on EC flux measurements conducted at two agricultural grassland sites in Finland.</p>
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