<p>The main hurdle in instrumentalizing agricultural soils to sequester atmospheric carbon is a development of methods to measure soil carbon stocks on farm level which are robust, scalable and widely applicable. Specifically, it is necessary that socio-economic barriers related to cost, usability and accessibility are overcome. We present the Wageningen Soil Carbon Stock pRotocol (SoilCASTOR), a method for soil carbon stock assessment using satellite data, direct soil measurements via mobile soil sensors and machine learning which can help overcome these socio-economic hurdles. The method has a low cost per hectare and uses plug-and play tools (soil scanner), which lower the threshold users need to overcome. The method has been tested and applied for multiple farms in Europe and the United states on agricultural fields with variable crop rotations, soil types and management history. Results show that the estimates are precise, repeatable and that the approach is rapidly scalable. Carbon stocks in the top 30 cm range between 1.8-6.1 kg C/hectare and resolution is up to 10x 10 meters. The precision of farm C stocks is below 5% enabling detection of SOC changes desired for the 4 per 1000 initiative. The assessment can be done robustly with as few as 0.5 samples (or 2-3 minutes) per hectare over a range of scales, for farms varying from 20 to 200 hectares.These findings can enable the structural and widespread implementation of carbon farming. This approach has recently been awareded the Bayer Grants4Tech innovation prize.</p>

<p><span xml:lang="EN-GB" data-contrast="auto"><span>I</span></span><span><span>mproved</span><span> soil and cropland management </span></span><span xml:lang="EN-GB" data-contrast="auto"><span>changes the</span></span><span><span> soil carbon stocks and thereby mitigate climate change. However, spatially explicit insights on management impacts as well as critical thresholds for optimum SOC levels are lacking, which is crucial for actionable changes in farming practices. In this study we </span></span><span xml:lang="EN-GB" data-contrast="auto"><span>unravelled the contribution of </span></span><span><span>soil texture, geohydrology and soil quality </span></span><span xml:lang="EN-GB" data-contrast="auto"><span>to changes in SOC </span><span>in the Netherlands </span><span>using a data-driven approach (using </span><span>XGBoost</span><span>) using </span><span>21.123</span><span> soil analyses done by agricultural laboratories.</span></span><span><span> The current C stock of the 0-30cm soil layer is 119 ton C ha</span></span><span><span>-1</span></span><span><span> and could be increased by 21 to 59 ton C ha</span></span><span><span>-1</span></span><span><span> depending on soil type, land use and the agronomic measures taken. The SOC saturation capacity, expressed as the ratio between the actual and potential SOC stock varied from 85 to 93% in grassland soils, from 55 to 83% in arable soils and from 69 to 91% in other land uses. On average, the actual C saturation degree was 75%. The key factors </span></span><span xml:lang="EN-GB" data-contrast="auto"><span>controlling the potential of soils to sequester additional carbon within environmental limits for N and P included</span></span><span><span> the</span></span><span xml:lang="EN-GB" data-contrast="auto"><span> crop sequence in the last decade,</span></span><span><span> soil texture (</span></span><span xml:lang="EN-GB" data-contrast="auto"><span>i.e.</span><span> oxide extractable aluminium, iron and phosphorus</span></span><span><span>), </span></span><span xml:lang="EN-GB" data-contrast="auto"><span>the acidity</span></span><span><span>, and groundwater depth. The data driven approach shows that spatially explicit recommendations for carbon farming are possible up to the farm and field scale, facilitating the implementation of carbon farming and the mitigation of climate change. When all agricultural fields are saturated with C</span><span>,</span><span> an equivalent of 257 </span><span>Mton</span><span> of CO</span></span><span><span>2</span></span><span><span> can be stored. </span></span><span> </span></p>

The main hurdle in instrumentalizing agricultural soils to sequester atmospheric carbon is the development of methods to measure soil carbon stocks which are robust, scalable, and widely applicable. Our objective is to develop an approach that can help overcome these hurdles. In this paper, we present the Wageningen Soil Carbon STOck pRotocol (SoilCASTOR). SoilCASTOR uses a novel approach fusing satellite data, direct proximal sensing-based soil measurements, and machine learning to yield soil carbon stock estimates. The method has been tested and applied in the USA on fields with agricultural land use. Results show that the estimates are precise and repeatable and that the approach could be rapidly scalable. The precision of farm C stocks is below 5% enabling detection of soil organic carbon changes desired for the 4 per 1000 initiative. The assessment can be done robustly with as few as 0.5 sample per hectare for farms varying from 20 to 150 hectares. These findings could enable the structural implementation of carbon farming.