Leigh A. Winowiecki

Digital Soil Map of the World

Increased demand and advanced techniques could lead to more refined mapping and management of soils. Soils are increasingly recognized as major contributors to ecosystem services such as food production and climate regulation (1, 2), and demand for up-to-date and relevant soil information is soaring. But communicating such information among diverse audiences remains challenging because of inconsistent use of technical jargon, and outdated, imprecise methods. Also, spatial resolutions of soil maps for most parts of the world are too low to help with practical land management. While other earth sciences (e.g., climatology, geology) have become more quantitative and have taken advantage of the digital revolution, conventional soil mapping delineates space mostly according to qualitative criteria and renders maps using a series of polygons, which limits resolution. These maps do not adequately express the complexity of soils across a landscape in an easily understandable way.

Mapping of soil organic carbon stocks for spatially explicit assessments of climate change mitigation potential

Current methods for assessing soil organic carbon (SOC) stocks are generally not well suited for understanding variations in SOC stocks in landscapes. This is due to the tedious and time-consuming nature of the sampling methods most commonly used to collect bulk density cores, which limits repeatability across large areas, particularly where information is needed on the spatial dynamics of SOC stocks at scales relevant to management and for spatially explicit targeting of climate change mitigation options. In the current study, approaches were explored for (i) field-based estimates of SOC stocks and (ii) mapping of SOC stocks at moderate to high resolution on the basis of data from four widely contrasting ecosystems in East Africa. Estimated SOC stocks for 0?30?cm depth varied both within and between sites, with site averages ranging from 2 to 8?kg?m?2. The differences in SOC stocks were determined in part by rainfall, but more importantly by sand content. Results also indicate that managing soil erosion is a key strategy for reducing SOC loss and hence in mitigation of climate change in these landscapes. Further, maps were developed on the basis of satellite image reflectance data with multiple R-squared values of 0.65 for the independent validation data set, showing variations in SOC stocks across these landscapes. These maps allow for spatially explicit targeting of potential climate change mitigation efforts through soil carbon sequestration, which is one option for climate change mitigation and adaptation. Further, the maps can be used to monitor the impacts of such mitigation efforts over time.

Community essay tools for enhancing interdisciplinary communication

Abstract This is a collaborative community essay, written by ten postdoctoral research fellows who had the opportunity to come together at Columbia University’s interdisciplinary Earth Institute. In many ways, we were different: our disciplinary backgrounds run the gamut in physical and social sciences; we study in different parts of the world, from sub-Saharan Africa to Latin America; we approach our work differently—some of us spend our days in the field collecting and analyzing soil samples, others conduct in-depth interviews in rural communities, while still others spend time in the lab elaborating formulas and crunching numbers. Yet, we found common ground: all of us are committed to addressing issues of sustainability in complex environments. As such, we wanted to harness our diversity and various strengths to bring together scientific, political, economic, demographic, geographic, ecological, and ethical perspectives on the challenges and opportunities of sustainable development. We remain ambitious in our aims. Nonetheless, we realized that our first task was figuring out how to communicate effectively across often disparate disciplines. This community essay chronicles that part of our journey. We hope it will be of use to others who endeavor to work across and beyond traditional academic disciplines.

Ecosystem Services in Agricultural Landscapes

There is a tenuous relationship between the world’s rural poor, their agriculture, and their surrounding environment. People reliant on farming for their livelihood can no longer focus on current food production without considering the ecosystem processes that ensure long-term production and provide other essential resources required for their well-being. Farmers are now expected to not only produce food, but also steward the landscape to ensure the provisioning of drinking water, wood products for construction and cooking, the availability of animal fodder, the capacity for flood attenuation, the continuity of pollination, and much more. Farmer stewardship of the landscape helps ensure ecological functions that, when beneficial to human well-being, are referred to as ecosystem services. Human activities strongly affect ecosystem services and there is often a resulting trade-off among their availability, which frequently results in the loss of many at the expense of few, most notably when producing food (Foley et al. 2005).

Improvement of spatial modelling of crop suitability using a new digital soil map of Tanzania

Climate change is projected to have widespread impacts on the climate suitability and geographical distribution of agricultural crops. Simulations were conducted on the suitability of common beans (Phaseolus vulgaris L.) in Tanzania under progressive climate change, taking into account a soil fertility constraint. The results were used to assess the effects of incorporating information on soil fertility, more specifically soil organic carbon (SOC) content, into the niche-based EcoCrop model, which was previously based only on climate data. Extending the model improved the correlation between predicted suitability and production statistics at the regional level. Simulated suitability was highly sensitive to SOC-related model parameters, implying that it is critical to incorporate these parameters in order to improve estimates of crop suitability. Simulations using the best parameterisation identified showed that low SOC is currently more limiting for common bean suitability than climate in 51% of the Tanzanian land area (protected areas excluded). However, future projections suggest that climate will be more limiting for the geographic distribution of common beans than SOC in the near future (2030). Spatial data on predicted SOC levels and other soil properties in future scenario modelling are needed for better identification of suitable areas for common bean production.

Survey data of intra-household decision making and smallholder agricultural production in Northern Uganda and Southern Tanzania

This article provides a description of intra-household survey data that were collected in Uganda and Tanzania in 2014 and 2015, respectively. The surveys were implemented using a structured questionnaire administered among 585 households in Uganda and 608 in Tanzania. Information on decision making processes in agricultural production was collected from the principal adult male and female decision-makers in each household. The survey consisted of two parts. Firstly, the decision-makers, both male and female of each household were jointly interviewed. Secondly, individual interviews were carried out, questioning the decision-makers separately. The datasets include both household and individual level data containing numeric, categorical and string variables. The datasets have been shared publicly on the Harvard dataverse.

Spatial Gradients of Ecosystem Health Indicators across a Human-Impacted Semiarid Savanna

Drivers of soil organic carbon (SOC) dynamics involve a combination of edaphic, human, and climatic factors that influence and determine SOC distribution across the landscape. High-resolution maps of key indicators of ecosystem health can enable assessments of these drivers and aid in critical management decisions. This study used a systematic field-based approach coupled with statistical modeling and remote sensing to develop accurate, high-resolution maps of key indicators of ecosystem health across savanna ecosystems in South Africa. Two 100-km landscapes in Bushbuckridge Local Municipality were surveyed, and 320 composite topsoil samples were collected. Mid-infrared spectroscopy was used to predict soil properties, with good performance for all models and root mean squared error of prediction (RMSEP) values of 1.3, 0.2, 5, and 3.6 for SOC, pH, sand, and clay, respectively. Validation results for the mapping of soil erosion prevalence and herbaceous cover using RapidEye imagery at 5-m spatial resolution showed good model performance with area under the curve values of 0.80 and 0.86, respectively. The overall (out-of-bag) random forest model performance for mapping of soil properties, reported using , was 0.8, 0.77, and 0.82 for SOC, pH, and sand, respectively. Calibration model performance was good, with RMSEP values of 2.6 g kg for SOC, 0.2 for pH, and 6% for sand content. Strong gradients of increasing SOC and pH corresponded with decreasing sand content between the study sites. Although both sites had low SOC overall, important driving factors of SOC dynamics included soil texture, soil erosion prevalence, and climate. These data will inform strategic land management decisions focused particularly on improving ecosystem conditions.

Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa : application of near-infrared spectroscopy

AimsStable carbon isotopes are important tracers used to understand ecological food web processes and vegetation shifts over time. However, gaps exist in understanding soil and plant processes that influence δ13C values, particularly across smallholder farming systems in sub-Saharan Africa. This study aimed to develop predictive models for δ13C values in soil using near infrared spectroscopy (NIRS) to increase overall sample size. In addition, this study aimed to assess the δ13C values between five vegetation classes.MethodsThe Land Degradation Surveillance Framework (LDSF) was used to collect a stratified random set of soil samples and to classify vegetation. A total of 154 topsoil and 186 subsoil samples were collected and analyzed using NIRS, organic carbon (OC) and stable carbon isotopes.ResultsForested plots had the most negative average δ13C values, −26.1‰; followed by woodland, −21.9‰; cropland, −19.0‰; shrubland, −16.5‰; and grassland, −13.9‰. Prediction models were developed for δ13C using partial least squares (PLS) regression and random forest (RF) models. Model performance was acceptable and similar with both models. The root mean square error of prediction (RMSEP) values for the three independent validation runs for δ13C using PLS ranged from 1.91 to 2.03 compared to 1.52 to 1.98 using RF.ConclusionsThis model performance indicates that NIR can be used to predict δ13C in soil, which will allow for landscape-scale assessments to better understand carbon dynamics.

Diversification and intensification of agricultural adaptation from global to local scales

Smallholder farming systems are vulnerable to a number of challenges, including continued population growth, urbanization, income disparities, land degradation, decreasing farm size and productivity, all of which are compounded by uncertainty of climatic patterns. Understanding determinants of smallholder farming practices is critical for designing and implementing successful interventions, including climate change adaptation programs. We examine two dimensions wherein smallholder farmers may adapt agricultural practices; through intensification (i.e., adopt more practices) or diversification (i.e. adopt different practices). We use data on 5314 randomly sampled households located in 38 sites in 15 countries across four regions (East and West Africa, South Asia, and Central America). We estimate empirical models designed to assess determinants of both intensification and diversification of adaptation activities at global scales. Aspects of adaptive capacity that are found to increase intensification of adaptation globally include variables associated with access to information and human capital, financial considerations, assets, household infrastructure and experience. In contrast, there are few global drivers of adaptive diversification, with a notable exception being access to weather information, which also increases adaptive intensification. Investigating reasons for adaptation indicate that conditions present in underdeveloped markets provide the primary impetus for adaptation, even in the context of climate change. We also compare determinants across spatial scales, which reveals a variety of local avenues through which policy interventions can relax economic constraints and boost agricultural adaptation for both intensification and diversification. For example, access to weather information does not affect intensification adaptation in Africa, but is significant at several sites in Bangladesh and India. Moreover, this information leads to diversification of adaptive activities on some sites in South Asia and Central America, but increases specialization in West and East Africa.

Updating Legacy Soil Maps for Climate Resilient Agriculture: A Case of Kilombero Valley, Tanzania

Since the first documented soil survey in Tanzania by Milne (J Ecol 35:192–265, 1936), a number of other soil inventory exercises at different scales have been made. The main challenge has been the fragmented nature of the often outdated detailed soil maps and small-scale less-informative country-wide soil maps. Recent advances in information and computational technology have created vast potential to collect, map, harness, communicate and update soil information. These advances present favorable conditions to support the already popular shift from qualitative (conventional) to quantitative (digital) soil mapping (DSM). In this study, two decision tree machine learning algorithms, J48 and Random Forest (RF), were applied to digitally predict k-means numerically classified soil clusters to update a soil map produced in 1959. Predictors were derived from 1 arc SRTM digital elevation data and a 5 m RapidEye satellite image. J48 and RF predicted the soil units of the legacy maps with greater detail. However, RF showed superiority for predicting clusters J48 could not predict and for showing higher pixel contiguity. No significant difference (P = 0.05) was observed between the soil properties of the predicted soil clusters and the actual field validation points. Young soils (Entisols and Inceptisols) were found to occupy about 56 % of the study site’s 30,000 ha followed by Alfisols, Mollisols and Vertisols at 31, 9 and 4 %, respectively. This study demonstrates the usefulness of DSM techniques to update conventionally prepared legacy maps to offer soil information at improved detail to agricultural land use planners and decision makers of Tanzania to make evidence-based decisions for climate-resilient agriculture and other land uses.