Carbon farming: Are soil carbon certificates a suitable tool for climate change mitigation?

Increasing soil organic carbon (SOC) stocks in agricultural soils removes carbon dioxide from the atmosphere and contributes towards achieving carbon neutrality. For farmers, higher SOC levels have multiple benefits, including increased soil fertility and resilience against drought-related yield losses. However, increasing SOC levels requires agricultural management changes that are associated with costs. Private soil carbon certificates could compensate for these costs. In these schemes, farmers register their fields with commercial certificate providers who certify SOC increases. Certificates are then sold as voluntary emission offsets on the carbon market. In this paper, we assess the suitability of these certificates as an instrument for climate change mitigation. From a soils' perspective, we address processes of SOC enrichment, their potentials and limits, and options for cost-effective measurement and monitoring. From a farmers' perspective, we assess management options likely to increase SOC, and discuss their synergies and trade-offs with economic, environmental and social targets. From a governance perspective, we address requirements to guarantee additionality and permanence while preventing leakage effects. Furthermore, we address questions of legitimacy and accountability. While increasing SOC is a cornerstone for more sustainable cropping systems, private carbon certificates fall short of expectations for climate change mitigation as permanence of SOC sequestration cannot be guaranteed. Governance challenges include lack of long-term monitoring, problems to ensure additionality, problems to safeguard against leakage effects, and lack of long-term accountability if stored SOC is re-emitted. We conclude that soil-based private carbon certificates are unlikely to deliver the emission offset attributed to them and that their benefit for climate change mitigation is uncertain. Additional research is needed to develop standards for SOC change metrics and monitoring, and to better understand the impact of short term, non-permanent carbon removals on peaks in atmospheric greenhouse gas concentrations and on the probability of exceeding climatic tipping points.

Evaluating ecosystem service changes in a frame of transportation development in Istanbul.

Rapid urbanization and growing transportation infrastructure in cities negatively affect ecosystems and their functions. Quantifying these effects is a prerequisite for integrating environmental considerations into all phases of transportation planning. However, in many developing or newly developed countries, research is lacking that helps to understand and manage the ecological impacts of transportation construction under local conditions. Presented research contributed to filling this gap by investigating the implications of growing transportation infrastructure on three ecosystem services: local climate regulation, erosion control, and photosynthesis potential. As a case study, we used spatial indicators to quantify changes in the supply of ecosystem services caused by the development of the 3rd Bosporus Bridge and its connecting highway in Istanbul, Turkiye. Our results indicate a substantial decrease in ecosystem services close to the transportation infrastructure, including a decrease in local climate regulation (- 5.4%), an increase in erosion (+ 9.4%), and a decline in photosynthesis potential or vegetation health (- 28%). Additionally, hotspots of ES supply change were detected. This study provides a blueprint for planning and impact mitigation studies.

Modelling climate change impacts on regional net primary productivity in Turkey.

This study projects and models the terrestrial net primary productivity (NPP) considering the representative concentration pathways (RCPs) scenarios of Turkey using remote-sensing-based biogeochemical modelling techniques. Changes in annual NPP between 2000-2010 and 2070-2080 were projected with the biogeochemical ecosystem model NASA-Carnegie Ames Stanford Approach (CASA). A multi-temporal data set, including 16-day MODIS composites with a spatial resolution of 250 m, was used within the CASA model. The 5th Assessment Report (AR5) of the IPCC presented several scenarios for RCPs named RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 that laid the foundation for the future climate projections. The futuristic NPP modelling was based on the assumptions of maintaining CO2 level in the range of 421 to 936 ppm and a rise in temperature from 1.1 to 2.6 °C. The NPP in Turkey averaged 1232 g C m2 year-1 as per the model results. Considering 2000-2010 as the baseline period, the NPP was modelled within the range of 9.6 and 316 g C m2 year-1. Modelled average NPP was 1332.4 g C m2 year-1 per year between 2061 and 2080. The forest productivity was also estimated to be increased up to 113 g C m-2 year-1 under the climate change scenarios. However, there were minor differences in the projected average NPP under the baseline period covering years from 2000 to 2080 from those under RCPs. It appeared that variation in temperature and precipitation as a result of climate change affected the terrestrial NPP. The regional environmental and socio-economic consequences of climate change on diverse landscapes such as Turkey were properly modelled and analysed to understand the spatial variation of climate change impacts on vegetation. Changes in NPP imply that forests in Turkey could be carbon sinks in the future as their current potential that would profile Turkey's climate mitigation. This is one of the pioneering studies to estimate the future changes of regional NPP in Turkey by integrating various spatial inputs and a biogeochemical model.

Spatial and temporal evaluation of soil erosion in Turkey under climate change scenarios using the Pan-European Soil Erosion Risk Assessment (PESERA) model.

The impacts of climate change on soil erosion are mainly caused by the changes in the amount and intensity of rainfall and rising temperature. The combination of rainfall and temperature change is likely to be accompanied by negative or positive variations in agricultural and forest management. Turkey contains vast fertile plains, high mountain chains and semi-arid lands, with a climate that ranges from marine to continental and therefore is susceptible to soil erosion under climate change, particularly on high gradients and in semi-arid areas. This study aims to model the soil erosion risk under climate change scenarios in Turkey using the Pan-European Soil Erosion Assessment (PESERA) model, predicting the likely effects of land use/cover and climate change on sediment transport and soil erosion in the country. For this purpose, PESERA was applied to estimate the monthly and annual soil loss for 12 land use/cover types in Turkey. The model inputs included 128 variables derived from soil, climate, land use/cover and topography data. The total soil loss from the land surface is speculated to be approximately 285.5 million tonnes per year. According to the IPCC 5th Assessment Report of four climate change scenarios, the total soil losses were predicted as 308.9, 323.5, 320.3 and 355.3 million tonnes for RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios respectively from 2060 to 2080. The predicted amounts of fertile soil loss from agricultural land in a year were predicted to be 55.5 million tonnes at present, and 62.7, 59.9, 61.7 and 58.1 under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 respectively. This confirms that approximately 30% of the total erosion occurs over the agricultural lands. In this respect, degraded forests, scrub and arable lands were subjected to the highest erosion rate (68%) of the total, whereas, fruit trees and berry plantations reflected the lowest erosion rates. Low soil organic carbon, sparse vegetation cover and variable climatic conditions significantly enhanced the erosion of the cultivated lands by primarily removing the potential food for organisms. Finally, process-based models offer a valuable resource for decision-makers when improving environmental management schemes and also decrease uncertainty when considering risks.

Response of the regression tree model to high resolution remote sensing data for predicting percent tree cover in a Mediterranean ecosystem.

Percent tree cover is the percentage of the ground surface area covered by a vertical projection of the outermost perimeter of the plants. It is an important indicator to reveal the condition of forest systems and has a significant importance for ecosystem models as a main input. The aim of this study is to estimate the percent tree cover of various forest stands in a Mediterranean environment based on an empirical relationship between tree coverage and remotely sensed data in Goksu Watershed located at the Eastern Mediterranean coast of Turkey. A regression tree algorithm was used to simulate spatial fractions of Pinus nigra, Cedrus libani, Pinus brutia, Juniperus excelsa and Quercus cerris using multi-temporal LANDSAT TM/ETM data as predictor variables and land cover information. Two scenes of high resolution GeoEye-1 images were employed for training and testing the model. The predictor variables were incorporated in addition to biophysical variables estimated from the LANDSAT TM/ETM data. Additionally, normalised difference vegetation index (NDVI) was incorporated to LANDSAT TM/ETM band settings as a biophysical variable. Stepwise linear regression (SLR) was applied for selecting the relevant bands to employ in regression tree process. SLR-selected variables produced accurate results in the model with a high correlation coefficient of 0.80. The output values ranged from 0 to 100 %. The different tree species were mapped in 30 m resolution in respect to elevation. Percent tree cover map as a final output was derived using LANDSAT TM/ETM image over Goksu Watershed and the biophysical variables. The results were tested using high spatial resolution GeoEye-1 images. Thus, the combination of the RT algorithm and higher resolution data for percent tree cover mapping were tested and examined in a complex Mediterranean environment.

The use of regression tree method for Sentinel-2 satellite data to mapping percent tree cover in different forest types.

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors. However, the limited number of PTC researches conducted with Sentinel-2 images reveals the importance of this study. This study aimed to demonstrate reliable PTC data in landscape planning or ecosystem modeling by introducing an advanced approach with high spatial, spectral, and temporal resolution and more cost-effective. In this study, a regression tree algorithm, one of the popular machine learning techniques for ecological modeling, was used to estimate the tree cover's dependent variable based on high-resolution monthly metrics' spectral signatures. Six frames of TripleSat images were used as training data in the regression tree. Monthly Sentinel-2 bands and produced metrics including NDVI, LAI, fCOVER, MSAVI2, and MCARI were almost the first time used as predictor variables. Stepwise linear regression (SLR) was applied to select these predictor bands in the regression tree and a correlation coefficient of 0.83 was obtained. Result PTC maps were produced and the results were evaluated based on coniferous and broadleaf. The results were tested using high spatial resolution TripleSat images and higher model accuracy was determined in both forest types. The high correlation is due to the Sentinel 2 satellite's band characteristics and the metrics are directly related to the tree cover. As a result, the high-accuracy availability of the Sentinel2 satellite is seen to map the PTC on a regional scale, including complex forest types between the Mediterranean and terrestrial transition climates.

Assessment of Soil Salinity Changes under the Climate Change in the Khorezm Region, Uzbekistan.

Soil salinity negatively affects plant growth and leads to soil degradation. Saline lands result in low agricultural productivity, affecting the well-being of farmers and the economic situation in the region. The prediction of soil salinization dynamics plays a crucial role in sustainable development of agricultural regions, in preserving the ecosystems, and in improving irrigation management practices. Accurate information through monitoring and evaluating the changes in soil salinity is essential for the development of strategies for agriculture productivity and efficient soil management. As part of an ex-ante analysis, we presented a comprehensive statistical framework for predicting soil salinity dynamics using the Homogeneity test and linear regression model. The framework was operationalized in the context of the Khorezm region of Uzbekistan, which suffers from high levels of soil salinity. The soil salinity trends and levels were projected under the impact of climate change from 2021 to 2050 and 2051 to 2100. The results show that the slightly saline soils would generally decrease (from 55.4% in 2050 to 52.4% by 2100 based on the homogeneity test; from 55.9% in 2050 to 54.5% by 2100 according to the linear regression model), but moderately saline soils would increase (from 31.2% in 2050 to 32.5% by 2100 based on the homogeneity test; from 31.2% in 2050 to 32.4% by 2100 according to the linear regression model). Moreover, highly saline soils would increase (from 13.4% in 2050 to 15.1% by 2100 based on the homogeneity test; from 12.9% in 2050 to 13.1% by 2100 according to the linear regression model). The results of this study provide an understanding that soil salinity depends on climate change and help the government to better plan future management strategies for the region.

Provision of metadata of European agricultural long-term experiments through BonaRes and EJP SOIL collaboration.

Agricultural Long-Term Experiments (LTEs) are crucial agricultural research infrastructures for monitoring the long term effects of management and environment on crop production and soil resources. We have compiled the meta-information of 616 LTEs from 30 different countries across Europe with a duration of typically 20 years, including clustered information of the European LTEs in different categories (management operations, land use, duration, status, etc.). It consists of the updated version of the dataset published by Grosse et al., (2020) but is extended by further LTE metadata, categories and research themes. Each set of metadata consists of up to 49 different attributes (categorical or numeric). Collected attributes were analyzed according to several research themes, including fertilization, crop rotation and tillage treatments. The collection of individual metadata was enlarged by the recent agreement between the BonaRes (www.bonares.de) and EJP SOIL (www.ejpsoil.eu) groups into the most comprehensive dataset in Europe, providing access to LTE and other, shorter running experiments. This dataset centralized past and existing information usually dispersed across several national actors. As such, it provides an extensive database that can be used by decision-makers, scientists, LTE owners and the public. The dataset can be updated in the future to foster networking and information exchange continuously.

Modeling Forest Productivity Using Envisat MERIS Data.

The aim of this study was to derive land cover products with a 300-m pixelresolution of Envisat MERIS (Medium Resolution Imaging Spectrometer) to quantify netprimary productivity (NPP) of conifer forests of Taurus Mountain range along the EasternMediterranean coast of Turkey. The Carnegie-Ames-Stanford approach (CASA) was usedto predict annual and monthly regional NPP as modified by temperature, precipitation,solar radiation, soil texture, fractional tree cover, land cover type, and normalizeddifference vegetation index (NDVI). Fractional tree cover was estimated using continuoustraining data and multi-temporal metrics of 47 Envisat MERIS images of March 2003 toSeptember 2005 and was derived by aggregating tree cover estimates made from high-resolution IKONOS imagery to coarser Landsat ETM imagery. A regression tree algorithmwas used to estimate response variables of fractional tree cover based on the multi-temporal metrics. This study showed that Envisat MERIS data yield a greater spatial detailin the quantification of NPP over a topographically complex terrain at the regional scalethan those used at the global scale such as AVHRR.