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The few percent of soil organic carbon (SOC) among mineral components form the interface of climate, plant growth, soil biological processes, physical transport infrastructure, and chemical transformations. We explore maps, models, myths, motivation, means of implementation, and modalities for transformation. Theories of place relate geographic variation in SOC to climate, soil types, land cover, and profile depth. Process-level theories of biophysical change and socioeconomic theories of induced change explain SOC transitions that follow from land use change when a declining curve is bent and recovery toward SOC saturation starts. While the desirability of recovering from SOC deficits has been mainstreamed into climate policy, the effectiveness of proposed measures taken remains contested. Process-level requirements for transitions at plot and landscape scales remain uncertain. Expectations of policy-induced SOC transitions have to align with national cross-sectoral C accounting and be managed realistically with land users (farmers) and commodity supply chains (private sector, consumers).

PR, IFPRI3; ISI; CRP5; G Cross-cutting gender theme, Natural Resources and Resilience Strategies (NRRS); Transformation Strategies, CGIAR Research Program on Water, Land and Ecosystems (WLE), Restoration of degraded land is key to enhancing land productivity and farmers' wellbeing in sub-Saharan Africa. Evidence shows that the benefits of land restoration are tremendous, ranging from biophysical benefits in soil health, agricultural productivity, ecosystem services, to socio-economic dimensions such as improving farmers’ income and livelihoods. Yet one issue that is rarely considered is how the outcomes of restoration initiatives affect different social groups, specifically women, men, and the marginalized. Our study aims to understand the extent of gender and social inclusion in land restoration studies in Ethiopia. Through analyzing 314 peer-reviewed scientific articles on land restoration initiatives and studies for Ethiopia published between 1985 and 2020, we aimed to: i) develop a novel approach that would allow for the semi-automatization of analyzing gender aspects in a large set of scientific documents using machine learning techniques, ii) assessed the current evidence on gender integration in peer-reviewed studies related to RDL, and iii) identified what gender issues are discussed in RDL literature. We employed text mining techniques to analyze the literature for keywords and to classify articles into three main classes: gender blind, gender sensitive, and gender transformative, based on the extent to which gender and social inclusion keywords appeared in the studies. Our analysis showed that gender and social inclusion are rarely considered in landscape restoration studies in Ethiopia, as most studies and interventions are heavily focused on the biophysical and environmental aspects of restoration. Despite an exponential increase in the use of gender and social inclusion keywords after 2005, most of the research (58 percent of articles) remains gender blind. The most frequently used related terms remained generic, e.g. community, access, participatory, and tenure. Only 38% of the studies could be classified as gender sensitive to a certain de

Digital soil mapping (DSM) approaches provide soil information by utilising the relationship between soil properties and environmental variables. Calibration of DSM models requires measurements that may often have substantial measurement errors which propagate to the DSM outputs and need to be accounted for. This study applied a geostatistical-based DSM approach that incorporates measurement error variances in the covariance structure of the spatial model, weights measurements in accordance with their measurement accuracies and assesses the effects of measurement errors on the accuracies of DSM outputs. The method was applied in the Western Cameroon, where soil samples from 480 locations were collected and analysed for pH, clay and soil organic carbon (SOC) using conventional and mid-infrared spectroscopy methods. Variogram parameters and regression coefficients were estimated using residual maximum likelihood under two scenarios: with and without taking measurement errors into account. Performance of the spatial models in the two scenarios was compared using validation metrics obtained with three types of cross-validation. Acknowledging measurement errors impacted the regression coefficients and influenced the variogram parameters by reducing the nugget and sill variance for the three soil properties. Validation metrics including mean error, root mean square error and model efficiency coefficient were quite similar in both scenarios, but the prediction uncertainties were more realistically quantified by the models that account for measurement errors, as indicated by accuracy plots. There were relatively small absolute differences in predicted values of soil properties of up to 0.1 for pH, 1.6% for clay and 2 g/kg for SOC between the two scenarios. We emphasised the need of incorporating measurement errors in DSM approaches to improve uncertainty quantification, particularly when applying spectroscopy for estimating soil properties. Further development of the approa

PR, IFPRI3; ISI; CRP2; 4 Transforming Agricultural and Rural Economies; DCA; IFPRIOA, DSGD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Lack of accurate information about soil nutrient deficiencies coupled with limited access to appropriate fertilizers could lead to mismatch between soil nutrient deficiencies (requirements) and fertilizer applications. Such anomalies and mismatches are likely to have important implications for agricultural productivity. In this article we use experimental (spectral soil analysis) data from Ethiopia to examine farmers’ response to soil nutrient deficiencies and its implications for yield responses. We find that farmers’ response to macronutrient (nitrogen and phosphorus) deficiencies is not always consistent with agronomic recommendations. For example, we find that farmers in our sample are applying nitrogen fertilizers to soils lacking phosphorus, potentially due to lack of information on soil nutrient deficiencies or lack of access to appropriate fertilizers in rural markets. On the other hand, farmers respond to perceivably poor-quality and acidic soils by applying higher amount of nitrogen and phosphorus fertilizers. We further show that such mismatches between fertilizer applications and soil macronutrient deficiencies are potentially yield-reducing. Those farmers matching their soil nutrient deficiencies (requirements) and fertilizer application are likely to enjoy additional yield gains. Marginal yield response associated with nitrogen (phosphorus) application increases with nitrogen (phosphorus) deficiency in soils. Similarly, we find that farmers’ response to acidic soils is not yield-enhancing. These findings suggest that such mismatches may explain heterogeneities in marginal returns to chemical fertilizers and the observed low adoption rates of chemical fertilizers in sub-Saharan Africa.