The blurred boundaries of ecological, sustainable, and agroecological intensification: a review

International audienceAbstractThe projected human population of nine billion by 2050 has led to ever growing discussion of the need for increasing agricultural output to meet estimated food demands, while mitigating environmental costs. Many stakeholders in agricultural circles are calling for the intensification of agriculture to meet these demands. However, it is neither clear nor readily agreed upon what is meant by intensification. Here, we compare the three major uses, ‘ecological intensification’, ‘sustainable intensification’ and ‘agroecological intensification’, by analysing their various definitions, principles and practices, and also their historical appearance and evolution. We used data from the scientific literature, the grey literature, the websites of international organizations and the Scopus and FAOLEX databases. Our major findings are: (1) sustainable intensification is the most frequently used term so far. (2) The three concepts ecological intensification, sustainable intensification and agroecological intensification overlap in terms of definitions, principles and practices, thus creating some confusion in their meanings, interpretations and implications. Nevertheless, some differences exist. (3) Sustainable intensification is more widely used and represents in many cases a rather generalised category, into which most current farming practices can be put so long as sustainability is in some way addressed. However, despite its wider use, it remains imprecisely defined. (4) Ecological and agroecological intensification do introduce some major nuances and, in general, more explicitly stated definitions. For instance, ecological intensification emphasizes the understanding and intensification of biological and ecological processes and functions in agroecosystem. (5) The notion of agroecological intensification accentuates the system approach and integrates more cultural and social perspectives in its concept. (6) Even if some boundaries can be seen, confusion is still predominant in the use of these terms. These blurred boundaries currently contribute to the use of these terms for justifying many different kinds of practices and interventions. We suggest that greater precision in defining the terms and the respective practices proposed would indicate more clearly what authors or institutions are aiming at with the proposed intensification. In this sense, we provide new definitions for all three intensification concepts based on the earlier ones

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

CENP-C recruits M18BP1 to centromeres to promote CENP-A chromatin assembly

CENP-C provides a link between existing CENP-A chromatin and the proteins required for new CENP-A nucleosome assembly

Symposium review: uncertainties in enteric methane inventories,measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes

Ecology of natural climate solutions, The: quantifying soil carbon and biodiversity benefits

2021 Spring.Includes bibliographical references.Achieving net zero greenhouse gas emission by 2050 will require simultaneous emissions reductions and carbon dioxide removal from the atmosphere. Natural climate solutions offer the most mature opportunities to remove atmospheric carbon and sequester it in woody biomass and soils but currently these options remain at low levels of adoption in the United States. To increase the uptake of these practices by growers, there needs to be greater confidence in the expected soil carbon benefits and improved understanding of potential environmental tradeoffs from these strategies across management and environmental contexts. This dissertation quantified the influence of management decisions and environmental variables on soil carbon responses under two proposed agricultural natural climate solutions: inclusion of cover crops and additions of organic amendments. The ecological and biodiversity co-benefits under these practices were also examined. Using a meta-analysis approach, the first chapter analyzed soil carbon responses to cover crop management decisions and environmental variables. Across 181 observations of 40 publications from temperate climates, inclusion of cover crops in cropping systems increased soil organic carbon stocks from 0-30 cm by twelve percent relative to a similarly managed system without cover crops. Management and environmental variables were responsible for variation in soil C responses across studies. The second chapter evaluated the application of organic amendments to improved and semi-native pastures at a semi-arid experimental site in northern Colorado. Over eight years and two applications of a high-quality organic amendment, soil organic carbon stocks as quantified by equivalent soil mass increased 0.7 Mg C ha-1 yr-1 from 0-20 cm under the organic amendment in the improved pasture relative to the control. After accounting for the additions of carbon from the two amendment applications, soil organic carbon stocks in the improved pasture increased by 0.46 Mg C ha-1 yr-1 from 0-20 cm. In contrast, there was no net change of soil carbon stocks in the semi-native pasture. The third chapter examined changes in plant and soil community composition and function after nitrogen application at the same experimental site. A single organic nitrogen addition to the improved pasture increased forage production, plant diversity, and soil microbial community composition and function. The stronger initial plant responses and the gradual change in microbial community composition and function suggests a plant-mediated response to organic nitrogen in this system, which likely impacted soil carbon cycling. Water-limited, semi-native pastures appear to be more resistant to change under one-time organic and inorganic nitrogen additions than irrigated, improved pastures. The final chapter of this dissertation compared two recommended approaches by the Food and Agriculture Organization of the United Nations for quantifying livestock production system impacts on biodiversity. The results illustrated how indicator selection and functional unit may result in discrepancies between the two methods. Together, these findings contribute to a growing body of scientific evidence in support of natural climate solutions for their climate and environmental co-benefits

Strategies to mitigate enteric methane emissions by ruminants - a way to approach the 2.0°C target.

Abstract Ruminant livestock enteric fermentation contributes approximately one-third of the global anthropogenic methane (CH 4 ) emissions and is projected to increase significantly to meet the increasing demand for animal-sourced protein. Methane, a short-lived greenhouse gas, needs to be reduced -24 to -47% by 2050 relative to 2010 to meet the 2.0°C target. This study describes the results of a comprehensive meta-analysis to determine effective mitigation strategies. The database included findings from 425 peer-reviewed studies (1963 to 2018). Mitigation strategies were classified into three main categories [animal and feed management, diet formulation, and rumen manipulation (additives and methods used to modify the rumen)] and up to five subcategories (98 total mitigation strategy combinations). A random-effects meta-analysis weighted by inverse variance was carried out (Comprehensive Meta-Analysis, V3.3.070). Five feeding strategies, namely CH 4 inhibitors, oils and fats, oilseeds, electron sinks, and tanniferous forages, decreased absolute CH 4 emissions by on average -21% (range -12 to -35%) and CH 4 emissions per unit of product (CH 4 I; meat or milk) by on average -17% (range -12 to -32%) without negatively affecting animal production (weight gain or milk yield). Furthermore, three strategies, namely decreasing dietary forage-to-concentrate ratio, increasing feeding level, and decreasing grass maturity, decreased CH 4 I by on average -12% (range -9 to -17%) and increased animal production by on average 45% (range 9 to 162%). The latter strategies are central to meeting the increasing demand for animal-sourced food. All strategies, but CH 4 inhibitors, can be implemented now and offer immediate approaches for combating global warming

Promising nutritional strategies to reduce enteric methane emission from ruminants – a meta-analysis

International audienceDecreasing enteric CH4 emissions is important in mitigating the environmental impact of livestock farming. The present meta-analysis examined effects of nutritional mitigation practices on absolute CH4 emissions (g/animal/d) and CH4 yield [g CH4/kg dry matter intake (DMI)] as well as on DMI (kg/d), average daily gain (kg/d), milk production (kg/d), and neutral detergent fiber digestibility (%). The database for this analysis consisted of over 400 studies. Only studies that reported statistical variance were included in the analysis (295 studies and 644 treatment mean comparisons). A standard random-effects meta-analysis weighted by inverse variance was carried out. The effects of the standardized mean difference (SMD) were classified as small (≤-0.2 and >-0.5), medium (≤-0.5 and >-0.8), and large (≤-0.8). Of the analyzed treatments, inclusion of chemical inhibitors, electron sinks, and lipids had a large effect on absolute CH4 emissions (-2.1 ± 0.5, -1.6 ± 0.2, and -1.3 ± 0.2 SMD ± SE, respectively; P 0.15), whereas electron sinks and lipids led to a small decrease in DMI (-0.2 ± 0.1, and -0.4 ± 0.1 SMD ± SE, respectively; P ≤0.01) without affecting animal productivity (P >0.05). Although these nutritional strategies effectively reduced CH4 emissions without compromising animal productivity, their adoption will largely depend on their economic feasibility

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

International audienceAbstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century

A gravitational wave detector operating beyond the quantum shot-noise limit: Squeezed light in application

This contribution reviews our recent progress on the generation of squeezed light [1], and also the recent squeezed-light enhancement of the gravitational wave detector GEO 600 [2]. GEO 600 is currently the only GW observatory operated by the LIGO Scientific Collaboration in its search for gravitational waves. With the help of squeezed states of light it now operates with its best ever sensitivity, which not only proves the qualification of squeezed light as a key technology for future gravitational wave astronomy but also the usefulness of quantum entanglement