Relating the impacts of regenerative farming practices to soil health and carbon sequestration on Gotland, Sweden

Land degradation, greenhouse gas emissions and biodiversity loss through agriculture are some of the greatest challenges we are facing today. Fertile and productive soils are the basis of life on this planet and need to be protected and restored to support a growing population and lower negative impacts of climate change. Regenerative agriculture (RA) claims to improve environmental, social, and economic facets of food production. Its emphasis lies on carbon sequestration for climate change mitigation, biodiversity, and food security through the regeneration of degraded land. The concept of regenerative agriculture has gained attention both in mainstream media and in academic literature in recent years. However, there is no uniform definition of the term so far, and further there is a lack of comprehensive scientific studies on “real-life” farms that are changing their management from conventional to regenerative practices. This thesis investigates the contemporary and historical context of the emerging term regenerative agriculture and identifies the main themes, movements, and debates associated with it by a broad literature research. Further, we compare regenerative farms with conventional farms on Gotland, Sweden in order to draw first conclusions about the impact of certain farming practices on soil physical, chemical, and biological parameters. The soil health on 24 different plots is assessed by a variety of indicators, i.a. total, organic, active, and microbial biomass carbon, C:N ratio, wet aggregate stability, root depth and abundance, earthworm number, nutrient leaching, and soil texture. These parameters are related to four main management practices: application of organic matter, soil disturbance through tillage, crop diversity, and share of legumes through a principal component analysis and multiple linear regressions. We found that the amount of carbon added to the soil had a significant impact on several soil health indicators, mainly organic and active carbon, bulk density, number of earthworms, root abundance, water infiltration, and vegetation density. Reduced tillage was connected to higher wet aggregate stability, and vegetation density. These findings need to be confirmed in the coming years; however, they show that higher organic inputs and less soil disturbance generally had a positive impact on soil health on the investigated farms. Soil sampling will be continued on the same plots in the future to thoroughly investigate the impacts over a longer time period, as the thesis is part of the project Time Zero! Land surveys during farm conversion from abandoned land to regenerative agriculture performed at the Department of Soil and Environment at the Swedish University of Agriculture, Uppsala

The potential of regenerative agriculture to improve soil health on Gotland, Sweden

Background Regenerative agriculture has gained attention in mainstream media, academic literature, and international politics in recent years. While many practices and outcomes relate to RA, there is no uniform definition of the term, and only a few comprehensive scientific studies exist of "real-life" farms and the complexity of what is considered regenerative management and its impact on soil health. Aims This study aimed to relate the impact of single and various combinations of regenerative management practices to soil health indicators on Gotland, Sweden. Methods Soil health of 17 farm fields and six gardens was assessed on 11 farms that had applied regenerative agricultural practices for zero to 30 years. We measured a variety of physical (bulk density , infiltration rate, wet aggregate stability, root depth and abundance, penetration resistance), chemical (pH, electric conductivity, C:N ratio, total organic carbon ) and biological (earthworm abundance, active carbon, microbial biomass carbon) soil indicators. These parameters were related to regenerative practices (reduced tillage, application of organic matter , livestock integration, crop diversity, and share of legumes and perennials) through a combination of hierarchical clustering, Analysis of Variance and Tukey's tests, principal component analysis, and multiple linear regressions. Results At our study sites, the application of organic matter had a positive impact on bulk density, carbon-related parameters, wet aggregate stability, and infiltration rate, while reduced tillage and increased share of perennials combined had a positive impact on vegetation density, root abundance and depth, and wet aggregate stability. The field plots were divided into four clusters according to their management, and we found significantly higher values of total organic carbon (*), C:N (*), infiltration rate (**), and earthworm abundance (*) for crop-high-org-input, the management cluster with highest values of organic matter application and no tillage. We found significantly higher values of vegetation density (***) and root abundance (**) for perm-cover-livestock, the cluster with no tillage, integration of livestocks, and permanent cover (*** p 0.1). Conclusions We support existing knowledge on positive impacts of regenerative practices, namely, the addition of an organic amendment that improved C-related parameters, as well as the positive effects on soil structure of reduced tillage in combination with an increased share of perennials. We argue for an outcome-based, and principle-led concept of RA as a context-dependent agricultural approach

Are greenhouse gas fluxes lower from ley or perennial fallow than from arable organic soils? A systematic review protocol

Background Cultivated peatlands are widespread in temperate and boreal climate zones. For example, in Europe about 15% of the pristine peatland area have been lost through drainage for agricultural use. When drained, these organic soils are a significant source of greenhouse gas (GHG) emissions. To reach climate goals, the agricultural sector must reduce its GHG emissions, and one measure that has been discussed is changing land use from cropland to ley production or perennial green fallow. This management change leads to lower reported emissions, at least when using the IPCC default emission factors (EF) for croplands and grasslands on organic soils (IPCC 2014). However, there was a limited background dataset available for developing the EFs, and other variables than management affect the comparison of the land use options when the data originates from varying sites and years. Thus, the implications for future policies remain uncertain. This protocol describes the methodology to conduct a systematic review to answer the question of whether ley production or perennial green fallow can be suggested as a valid alternative to annual cropping to decrease GHG emissions on organic soils in temperate and boreal climate.Methods Publications will be searched in different databases and bibliographies of relevant review articles. The comprehensiveness of the search will be tested through a list of benchmark articles identified by the protocol development team. The screening will be performed at title and abstract level and at full text level, including repeatability tests. Eligible populations are organic agricultural soils in temperate and boreal climate regions. Interventions are grasslands without tillage for at least 3 years, and comparators are annual cropping systems within the same study as the intervention. The outcome must be gas fluxes of either carbon dioxide (CO2), nitrous oxide (N2O), or methane (CH4), or any combination of these gases. Studies will go through critical appraisal, checking for internal and external validity, and finally data extraction. If possible, a meta-analysis about the climate impact of perennial green fallow compared to annual cropping on organic soils will be performed

SoilCompDB: Global soil compressive properties database. Version 1.0

<p><strong>Data collection and processing</strong></p><p>Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022.  A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection.  After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database.  We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries.</p><p><strong>Time and place</strong></p><p>The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries.  </p><p><strong>Instruments</strong></p><p>The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamandé et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006).</p><p><strong>Resources</strong></p><p>Web of Science, Scopus – literature search</p><p>Endnote Web – removal of duplicates</p><p>Rayyan software – initial paper selection based on title and abstract</p><p>WebPlotDigitizer – data extraction from figures</p><p>Microsoft Access – database platform</p><p><strong>Description of the collected data (column, unit, and description)</strong></p><p>Sample ID-    A unique identification number assigned to each individual sample within the database                                                                        </p><p>Study ID- Identification number assigned to each research study in the database</p><p>Reference - Research paper reference</p><p>Year - Year of research paper publication                 </p><p>Language - Language of the research paper              </p><p>Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers</p><p>Soil classification (original in paper) - Soil classification described in research paper </p><p>Soil classification (convertion to Soil Taxonomy orders) -  Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA)          </p><p>Location - Study location country   </p><p>Texture classification (USDA) - Soil textural classification according USDA</p><p>Texture  classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay</p><p>Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm)                </p><p>Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) </p><p>Sand (USDA) - % - Soil sand content (weight based)  - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)</p><p>USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated</p><p>Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available</p><p>Clay - g kg-1 - Soil clay content - original in the paper</p><p>Clay class upper boundary - µm - The clay class upper boundary informed in source publication</p><p>Silt - g kg-1 - Silt clay content - original in the paper</p><p>Silt class upper boundary - µm - The silt class upper boundary informed in source publication</p><p>Sand - Soil sand content - original in the paper</p><p>Sand class upper boundary - µm - The sand class upper boundary informed in source publication</p><p>Particle size data flag - =0 if no issues; =1 if there are issues (summing)</p><p>Sum particle size- g kg-1 - Sum of clay, silt, and sand content</p><p>Soil depth FROM – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected                     </p><p>Soil depth TO – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected                                  </p><p>Depth – cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm)                                                    </p><p>SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58                                            </p><p>SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations</p><p>Particle density - Mg m-3 - Soil particle density                     </p><p>Initial matric potential – hPa - Soil water matric potential before loading</p><p>log Initial matric potential - Soil water matric potential expressed by log                              </p><p>Wetness (based on initial matric potential) -  1=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa</p><p>Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density</p><p>Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported</p><p>Initial water content data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples                 </p><p>Initial bulk density - Mg m-3 - Soil bulk density before loading                     </p><p>Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used                                                                                  </p><p>Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density</p><p>Precompression stress – kPa - Precompression stress                                                                                         </p><p>Precompression stress (SD) – kPa - Standard deviation for precompression stress values reported in paper                                                                                        </p><p>Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Compression index - Compression index                                 </p><p>Compression index (SD) - Standard deviation of compression index values reported in paper                  </p><p>Compression index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Swelling index - Swelling index                            </p><p>Swelling index (SD) - Standard deviation of swelling index values reported in paper                                     </p><p>Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>N - Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported</p><p>Land use (paper) - Land use described in the research paper</p><p>Land use (categories) - Land use categorized</p><p>Land use standardized - Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna</p><p>Land use (number code) - Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation</p><p>Tillage system - Tillage system</p><p>Tillage system (arable soils) - Tillage system for arable soils classified as "conventional" and "conservation"</p><p>Coordinates -  Geographical coordinates  of study location</p><p>Climate - Climatic region classification: temperate, tropical, subtropical</p><p>Climatecod -    Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical</p><p>Sampling position (paper) - Field position where soil samples were collected with details described in the paper</p><p>Sampling position - Field position where soil samples were collected standardized</p><p>Treatment - Experimental treatment type where the soil samples were collected</p><p>Stress rate -  kPa - Stress applied in compressive tests </p><p>Minimum stress – kPa - Minimum stress applied in compressive tests</p><p>Maximum stress – kPa - Maximum stress applied in compressive tests</p><p>Number of stress rate steps - Number of steps in stepwise stress application procedure</p><p>Stess application type - 1=Stepwise stress 2=one sample per stress 3=Strain controlled</p><p>Stess application type – min - Time for stress application in each step in stepwise stress application procedure</p><p>Degree of deformation at the end of loading - % - Degree of deformation at the end of compressive test</p><p>Sample diameter – cm - Diameter of the soil samples</p><p>Sample height – cm - Height of the soil samples</p><p>Ratio sample diameter and height - Ratio between diameter and height of the soil samples</p><p>Sample volume - cm3 -Sample volume when the sample diameter and height are nor presented</p><p>Precompression stress calculation method - Calculation method of precompression stress</p><p>Precompression stress calculation method (number code) - Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamandé et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006)</p><p>Description of precompression stress calculation - Brief explanation of precompression stress calculation</p><p>Soil compressive curve components - Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain. </p><p>Soil compressive curve components (number code) - Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio</p><p>Curve components source - Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper</p><p>Compressive curve available - Original soil compressive curve available in the paper: 1= No 2=Yes</p><p>Comments - Brief comments on the paper</p><p><strong>Issues and remarks</strong></p><p>We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 μm, utilizing the k-nearest neighbor approach (referred to as "similarity method" by Nemes et al. (1999).  </p><p><strong>Reference</strong></p><p>Associação Brasileira de Normas Técnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990.</p><p>Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60–64.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954–960 10.1139/T2012-054. doi:10.1139/t2012-054</p><p>Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562–567.</p><p>Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139–151. doi:10.1016/0933-3630(95)00015-8</p><p>Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012</p><p>Lamandé, M., Schjønning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274.</p><p>Nemes, A.,  Wösten, J.H.M., Lilly, A.,  Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database. Geoderma 90: 187-202. 129 </p><p>O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 – 4):161 – 173.</p&gt