The difference in the matrix present in soil samples from different areas limits the performance of nutrient analysis via XRF sensors, and only a few strategies to mitigate this effect to ensure an accurate analysis have been proposed so far. In this context, this research aimed to compare the performance of different predictive models, including a simple linear regression (RS), multiple linear regression (MLR), partial least-squares regression (PLS), and random forest (RF) models for the prediction of Ca and K in agricultural soils. RS models were evaluated on XRF data without (RS1) and with (RS2) Compton normalization. In addition, it was assessed whether using soil texture information and/or vis–NIR spectra as auxiliary variables would optimize the predictive performance of the models. The results showed that all strategies allowed the mitigation of the matrix effect to some degree, enabling the determination of their Ca and K contents with excellent predictive performance (R² ≥ 0.84). The best performance was obtained using RS2 for the Ca prediction (R² = 0.92, RSME = 48.25 mg kg⁻¹ and relative improvement (RI) of 52.3% compared to RS1) and using an RF for the K prediction (R² = 0.84, RSME = 17.43 mg kg⁻¹ and RI of 24.3% compared to RS1). The results indicated that sophisticated models did not always perform better than linear models. Furthermore, using texture data and vis–NIR spectra as auxiliary data was promising only for the K prediction, which showed an error reduction in the order of 10%, contrasting with the Ca prediction, which did not reduce the prediction error by more than 1%. The best modeling approach in our study proved to be attribute-specific. These results give further insight into the development of intelligence modeling strategies for sensor-based soil analysis.

Summary: Climate change (CC) challenges food and climate through reduced crop yields and increasing production risk. Regenerative agriculture (RA) emerged as a pivotal strategy for enhancing crop productivity and soil organic carbon (SOC) sequestration, contributing to agriculture’s CC mitigation and resilience. Nevertheless, expanding RA’s main challenges is providing sufficient science-based decision support for farmers and other stakeholders. In this context, we present herein the largest public-private partnership in Brazil to conduct research in a multidisciplinary collaborative scientific network on RA and describe the Carbon Farming Program approaches. Bayer SA leads the initiative, which also includes 11 partner institutions (i.e., Universities, Research Institutions and Foundations, and Farmers organisations). The programme aims to assess the benefits of improvement of cropland management, intensified and biodiverse crop rotation plans on SOC, soil health, crop productivity, and profitability in a no-till system. The programme has a multi-scale approach with three main steps (‘Research Partners’, ‘On-Farm Research Sites’, and ‘Carbon Program at Scale’). In total, it encompasses 1,906 farmers and 232 000 hectares across the Brazilian edaphoclimatic conditions. The programme has gathered a large database, integrating SOC and fertility determinations, and crop yields, to derive a quantitative evaluation of the impacts of sustainable agricultural land management practices adoption. Moreover, the programme enabled breaking through the gap of quantitative knowledge for the development of a novel mathematical model to predict SOC dynamics for tropical agroecosystems. This is worth supporting assertive decisions along the specific planning to promote scalability in the insertion of Brazilian agriculture in the global C market.

Sugarcane straw removal for bioenergy production—especially second‐generation ethanol—is shown to be a promising pathway for decarbonization. However, indiscriminate straw removal can negatively affect soil‐related ecosystem services (SES), compromising the sustainability of the associated bioenergy production. Here, a comprehensive literature review was conducted to select and quantify the changes in agronomic and environmental indicators affected by low (≤ 1/3), moderate (> 1/3 to ≤ 2/3), and high (> 2/3) straw removal levels and the consequential impacts on eight SES. A quali‐quantitative approach was developed to generate an impact matrix that provides the direction of the effects (negative, neutral, or positive) and the associated confidence levels. Overall, the lowest impact on SES occurs under low straw removal with a neutral effect on C storage, nutrient cycling, weed control, greenhouse gas (GHG) mitigation, and provision of food and bioenergy. Water regulation, erosion control, and maintenance of soil biodiversity were the SES most negatively affected by straw removal. Moderate and high levels of straw removal negatively impact the maintenance of SES and compromise the sustainability of sugarcane cultivation areas, except for pest control and soil GHG emission mitigation. Finally, it was also discussed how the negative impacts of straw removal on SES could be mitigated or even reversed through the adoption of best management practices, such as cover crops, organic amendments, biological products (e.g., use of phosphate‐solubilizing bacteria and mycorrhizal fungi), reduced tillage, and machinery traffic control. Ultimately, the results of this study can be useful to guide decision‐making by farmers, investors, stakeholders, and policymakers toward sustainable bioenergy production that contributes to a low‐carbon economy and climate change mitigation.

Biofuels are essential to ensure the energy transition and mitigating of climate change. However, understanding the impact of land use change (LUC) and management practices on soil organic carbon (SOC) stocks is fundamental to ensuring well‐founded policymaking and assessing the sector's carbon footprint. Here, we conducted a meta‐analysis (511 pairwise observations) to obtain Brazil's SOC stock change factors (SOC_scf) for LUC and management practices in sugarcane fields. Our results showed that converting native vegetation to sugarcane reduced the SOC stock in all assessed periods. The conversion from annual crops to sugarcane showed a reduction in SOC stock in the first 10 years but with a recovery over time. The conversion of pasture to sugarcane reduced the SOC stock only in the 10–20‐year period and had a neutral effect in other periods evaluated. However, our dataset showed high variability in SOC_scf, with many observations indicating an increase in SOC stock, which is related to degraded pastures. We observed that the SOC accumulation rate for each ton of sugarcane straw was affected by the interaction between soil texture and precipitation. Regarding straw management, a low removal rate (< 34%) did not affect the SOC stock, while moderate (34%–66%) and high (> 66%) removal resulted in losses of 5.0% (SOC_scf 0.950) and 9.9% (SOC_scf 0.901), respectively. Our results also showed that reduced tillage and vinasse application increased SOC stocks by 24.0% (SOC_scf 1.24) and 10.0% (SOC_scf 1.10) respectively, proving to be good strategies to support C sequestration in sugarcane fields. Finally, we highlight that our results can contribute to the improvement of public policies and also be used in future life cycle assessment (LCA) and modeling studies, as they provide robust data to establishing regional SOC_scf induced by LUC and management practices, enhancing the reliability of the C footprint assessment of biofuel production.