Soil carbon under current and improved land management in Kenya, Ethiopia and India: Dynamics and sequestration potentials

Agriculture is a major contributor to climate change, emitting the three major greenhouse gases (GHGs) – carbon dioxide (CO2), methane and nitrous oxide – into the atmosphere. According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), the Agriculture, Forestry and Other Land Use sector “is responsible for just under a quarter (~10–12 Gt CO2eq/yr) of [all] anthropogenic GHG emissions mainly from deforestation and agricultural emissions from livestock, soil and nutrient management”. Land use change – often associated with deforestation – contributes about 11.2% to this share, while agricultural production is responsible for 11.8% (IPCC, 2014). To reduce emissions from agriculture, while providing and maintaining global food security, there is a growing interest to develop and promote low-emission greengrowth pathways for future agricultural production systems. Sub-Saharan Africa (SSA) faces two concerns in that respect: a) agriculture is the major emitter of GHGs on this sub-continent, and b) agriculture is largely underperforming. To feed a growing population, productivity and total production need to increase significantly. To achieve this while reducing emissions from agriculture at the same time is a major challenge. Climate-smart agriculture (CSA) sets out to address this challenge by transforming agricultural systems affected by the vagaries of climate change. CSA aims at improving food security and system’s resilience while addressing climate change mitigation

An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter

Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures

Plant and soil microfaunal biodiversity across the borders between arable and forest ecosystems in a Mediterranean landscape

International audienceThe distribution of organisms across ecosystem borders can be indicative of trophic interactions, food-web dynamics, and the potential for recovery after disturbance. Yet relatively little is known regarding patterns and ecology of belowground organisms across borders. Our hypothesis was that incremental zonation of vegetation and soil properties at the interface between cultivated fields and forests may facilitate the recolonization of a more complex soil faunal assemblage after disturbance ceases. Vegetation, soil characteristics, and soil nematodes (indicators of disturbance) were studied at the interface between arable and natural ecosystems (oak forest and maquis shrubland) in southwestern France. Sampling was along 23-m long transects, at six positions (center and edge of grain fields, both sides of field borders, and bands of shrub and forest vegetation) at four sites. Plant functional groups changed more markedly than species richness. Total soil carbon (C) and nematode biomass were 3.5 and 6 times higher in the forest than in the center of the cultivated fields. The nematode Structure Index gradually increased from fields to forests, along with higher total and labile soil C pools, litter, root C, and root C:N, and more negative root delta N-15. Microbivore nematodes were related to labile and total soil C. Structural equation modeling indicated that nematode predators and prey were both affected by total soil C, but proximity to the forest was important for predators, whereas plant community complexity was important for prey (i.e., microbivorous nematodes). The forested borders had minor effects on zonation of nematode assemblages and soil ecosystem services within the fields, yet woody vegetation may have facilitated recolonization by plants and soil fauna after tillage ceased and probably provided benefits for production of livestock (i.e., shade and erosion reduction) that were not measured. During plant succession, litter C and N apparently decomposed slowly into active forms in the soil, creating habitats for more K-selected, larger-bodied nematodes. Due to less cultivation and higher C inputs during the past 50 years, the more homogeneous landscape may promote more complex soil food webs, but less total agrobiodiversity, compared to the mosaic of diverse ecosystems that occurred in the ancient cultural landscape of the past

Beyond total carbon: conversion of amazon forest to pasture alters indicators of soil C cycling

It is well established that land use change (LUC) can impact soil organic carbon (SOC) in tropical regions, but the long-term effects of LUC on soil quality and C cycling remain unclear. Here, we evaluated how LUC affects soil C cycling in the Amazon region using a 100-year observational chronosequence spanning primary forest-to-pasture conversion and subsequent secondary forest succession. We found a surprising increase in topsoil SOC concentrations 60 years following conversion, despite major losses (> 85%) of forest-derived SOC within the first 25 years. Shifts in molecular composition of SOC, identified with diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, occurred in tandem with a significant decline in permanganate-oxidizable C (POXC) and β-glucosidase activity (per unit SOC), interpreted as a deceleration of soil C cycling after pasture grasses became the dominant source of C inputs to soil. Secondary forest succession caused rapid reversal to conditions observed under primary forest for β-glucosidase activity but not for SOC molecular composition (DRIFT spectroscopy), reflecting a long-lasting effect of LUC on soil C cycling. Our results show that rapid changes in the origin of SOC occur following deforestation with legacy effects on some indicators of C cycling (e.g. enzyme activity) but not others (e.g. molecular composition). This approach offers mechanistic understanding of LUC in the Amazon basin and can be used to help explain conflicting reports on how deforestation impacts SOC in the region

A scalable framework for quantifying field-level agricultural carbon outcomes

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N2O and CH4) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes

Assessing the sensitivity and repeatability of permanganate oxidizable carbon as a soil health metric: An interlab comparison across soils

Soil organic matter is central to the soil health framework. Therefore, reliable indicators of changes in soil organic matter are essential to inform land management decisions. Permanganate oxidizable carbon (POXC), an emerging soil health indicator, has shown promise for being sensitive to soil management. However, strict standardization is required for widespread implementation in research and commercial contexts. Here, we used 36 soils—three from each of the 12 USDA soil orders—to determine the effects of sieve size and soil mass of analysis on POXC results. Using replicated measurements across 12 labs in the US and the EU (n = 7951 samples), we quantified the relative importance of 1) variation between labs, 2) variation within labs, 3) effect soil mass, and 4) effect of soil sieve size on the repeatability of POXC. We found a wide range of overall variability in POXC values across labs (0.03 to 171.8%; mean = 13.4%), and much of this variability was attributable to within-lab variation (median = 6.5%) independently of soil mass or sieve size. Greater soil mass (2.5 g) decreased absolute POXC values by a mean of 177 mg kg−1 soil and decreased analytical variability by 6.5%. For soils with organic carbon (SOC) >10%, greater soil mass (2.5 g) resulted in more frequent POXC values above the limit of detection whereas the lower soil mass (0.75 g) resulted in POXC values below the limit of detection for SOC contents −1 while decreasing the analytical variability by 1.8%. In general, soils with greater SOC contents had lower analytical variability. These results point to potential standardizations of the POXC protocol that can decrease the variability of the metric. We recommend that the POXC protocol be standardized to use 2.5 g for soils <10% SOC. Sieve size was a relatively small contributor to analytical variability and therefore we recommend that this decision be tailored to the study purpose. Tradeoffs associated with these standardizations can be mitigated, ultimately providing guidance on how to standardize POXC for routine analysis.</p