1
|
Doddrell NH, Lawson T, Raines CA, Wagstaff C, Simkin AJ. Feeding the world: impacts of elevated [CO 2] on nutrient content of greenhouse grown fruit crops and options for future yield gains. HORTICULTURE RESEARCH 2023; 10:uhad026. [PMID: 37090096 PMCID: PMC10116952 DOI: 10.1093/hr/uhad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/13/2023] [Indexed: 05/03/2023]
Abstract
Several long-term studies have provided strong support demonstrating that growing crops under elevated [CO2] can increase photosynthesis and result in an increase in yield, flavour and nutritional content (including but not limited to Vitamins C, E and pro-vitamin A). In the case of tomato, increases in yield by as much as 80% are observed when plants are cultivated at 1000 ppm [CO2], which is consistent with current commercial greenhouse production methods in the tomato fruit industry. These results provide a clear demonstration of the potential for elevating [CO2] for improving yield and quality in greenhouse crops. The major focus of this review is to bring together 50 years of observations evaluating the impact of elevated [CO2] on fruit yield and fruit nutritional quality. In the final section, we consider the need to engineer improvements to photosynthesis and nitrogen assimilation to allow plants to take greater advantage of elevated CO2 growth conditions.
Collapse
Affiliation(s)
- Nicholas H Doddrell
- NIAB, New Road, East Malling, Kent, ME19 6BJ UK
- Department of Food and Nutritional Sciences, University of Reading, Whiteknights, Reading, Berkshire RG6 6DZ, UK
| | - Tracy Lawson
- School of Life Sciences, University of Essex, Colchester CO4 4SQ, UK
| | | | - Carol Wagstaff
- Department of Food and Nutritional Sciences, University of Reading, Whiteknights, Reading, Berkshire RG6 6DZ, UK
| | - Andrew J Simkin
- NIAB, New Road, East Malling, Kent, ME19 6BJ UK
- School of Biosciences, University of Kent, Canterbury, United Kingdom CT2 7NJ, UK
| |
Collapse
|
2
|
Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series. Sci Rep 2022; 12:14170. [PMID: 35986037 PMCID: PMC9391387 DOI: 10.1038/s41598-022-18288-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/09/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractDistinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener–Granger’s idea. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. However, it only works for one-dimensional time series. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC). We demonstrate that PCCC retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially unobserved variables which can be reconstructed using the embedding theorem. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples.
Collapse
|
3
|
Koutsoyiannis D, Onof C, Christofidis A, Kundzewicz ZW. Revisiting causality using stochastics: 2. Applications. Proc Math Phys Eng Sci 2022. [DOI: 10.1098/rspa.2021.0836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In a companion paper, we develop the theoretical background of a stochastic approach to causality with the objective of formulating necessary conditions that are operationally useful in identifying or falsifying causality claims. Starting from the idea of stochastic causal systems, the approach extends it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. The framework developed is applicable to large-scale open systems, which are neither controllable nor repeatable. In this paper, we illustrate and showcase the proposed framework in a number of case studies. Some of them are controlled synthetic examples and are conducted as a proof of applicability of the theoretical concept, to test the methodology with
a priori
known system properties. Others are real-world studies on interesting scientific problems in geophysics, and in particular hydrology and climatology.
Collapse
Affiliation(s)
- Demetris Koutsoyiannis
- Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Athens, Greece
| | - Christian Onof
- Department of Civil and Environmental Engineering, Faculty of Engineering, Imperial College London, London, UK
| | - Antonis Christofidis
- Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Athens, Greece
| | - Zbigniew W. Kundzewicz
- Meteorology Lab, Department of Construction and Geoengineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznan University of Life Sciences, Poznań, Poland
| |
Collapse
|
4
|
Koutsoyiannis D, Onof C, Christofides A, Kundzewicz ZW. Revisiting causality using stochastics: 1. Theory. Proc Math Phys Eng Sci 2022. [DOI: 10.1098/rspa.2021.0835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Causality is a central concept in science, in philosophy and in life. However, reviewing various approaches to it over the entire knowledge tree, from philosophy to science and to scientific and technological applications, we locate several problems, which prevent these approaches from defining sufficient conditions for the existence of causal links. We thus choose to determine necessary conditions that are operationally useful in identifying or falsifying causality claims. Our proposed approach is based on stochastics, in which events are replaced by processes. Starting from the idea of stochastic causal systems, we extend it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. Theoretical considerations allow the development of an effective algorithm, applicable to large-scale open systems, which are neither controllable nor repeatable. The derivation and details of the algorithm are described in this paper, while in a companion paper we illustrate and showcase the proposed framework with a number of case studies, some of which are controlled synthetic examples and others real-world ones arising from interesting scientific problems.
Collapse
Affiliation(s)
- Demetris Koutsoyiannis
- Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Athens, Greece
| | - Christian Onof
- Department of Civil and Environmental Engineering, Faculty of Engineering, Imperial College London, London, England
| | - Antonis Christofides
- Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Athens, Greece
| | - Zbigniew W. Kundzewicz
- Meteorology Lab, Department of Construction and Geoengineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznan University of Life Sciences, Poznań, Poland
| |
Collapse
|
5
|
Cumulative Emissions of CO2 for Electric and Combustion Cars: A Case Study on Specific Models. ENERGIES 2022. [DOI: 10.3390/en15072703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This work includes calculations of the cumulative CO2 emissions of two comparable cars—the VW Golf VII—one with a combustion engine and the other with an electric motor. Calculation of CO2 emissions was performed, taking into account the stages of production, utilization and use of the above-mentioned vehicles. For the use phase, it was assumed that the total mileage of the car will be 150,000 km over 10 years. For the electric vehicle, calculations were made assuming five different sources of electricity (from coal only, from natural gas only, from PV and wind turbines, an average mix of European power sources and an average mix of Polish power sources; W1–W5 designations, respectively). For individual sources of electricity, cumulative CO2 emissions were taken into account, that is, resulting both from the production of electricity and the use of the resources (for example, technical service per 1 kWh of electricity produced). The obtained results of the analysis show that for the adopted assumptions regarding operation, for variants W2–W5, the use of an electric car results in lower cumulative CO2 emission than a the use of a combustion car. For a combustion car, the value was 37,000 kg-CO2, and for an electric car, depending on the variant, the value was 43, 31, 16, 23 and 34 thousand kg-CO2 for variants W1 to W5, respectively. Based on the emissions results obtained for individual stages of the use of selected vehicles, a comparative analysis of cumulative CO2 emissions was performed. The purpose of this analysis was to determine whether the replacement of an existing combustion car (that has already been manufactured; therefore, this part of the analysis does not include CO2 emissions in the production stage) with a new electric car, which has to be manufactured, therefore associated with additional CO2 emissions, would reduce cumulative CO2 emissions. Considering three adopted average annual car mileages (3000, 7500 and 15,000 km) and the previously described power options (W1–W5), we sought an answer as to whether the use of a new electric car would be burdened with lower cumulative CO2 emissions. In this case we assumed an analysis time of 15 years. For the worst variant from the point of view of CO2 emissions (W1, electricity from coal power sources only), further use of a combustion car is associated with lower cumulative CO2 emissions than the purchase of a new electric car over the entire analyzed period of 15 years. In turn, for the most advantageous variant (W3, electricity from PV or wind power sources) with an annual mileage of 3000 km, the purchase of a new electric car results in higher cumulative CO2 emissions throughout the analyzed period, whereas for an annual milage of 7500 or 15,000 km, replacing the car with an electric car “pays back” in terms of cumulative CO2 emissions after 8.5 or 4 years, respectively.
Collapse
|
6
|
Sci: An Inclusive, Multidisciplinary Scientific Journal. SCI 2022. [DOI: 10.3390/sci4010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Sci (ISSN 2413-4155) is an international, open-access journal that covers most fields of scientific research. It has set out to challenge the conventional single- and double-blind peer review processes by adopting a post-publication public peer review (P4R) model. The model faced some difficulties with indexing and archiving services, prolongated the peer review process and its transparency received some opposition. It was therefore necessary to revisit the P4R model and modify it, resulting in the hybrid model (P4R hybrid) which is implemented in Sci today. Sci remains open to the whole scientific community as an inclusive and multidisciplinary scientific journal. In this context, we present you with six valuable contributions to the first Special Issue of Feature Papers Editors Collection 2020. The topics of the contributions address relevant and compelling issues ranging from data protection, material science, COVID-19 to the environment and climate change.
Collapse
|
7
|
Abstract
Stips et al. (2016) use information flows (Liang (2008, 2014)) to establish causality from various forcings to global temperature. We show that the formulas being used hinge on a simplifying assumption that is nearly always rejected by the data. We propose the well-known forecast error variance decomposition based on a Vector Autoregression as an adequate measure of information flow, and find that most results in Stips et al. (2016) cannot be corroborated. Then, we discuss which modeling choices (e.g., the choice of CO2 series and assumptions about simultaneous relationships) may help in extracting credible estimates of causal flows and the transient climate response simply by looking at the joint dynamics of two climatic time series.
Collapse
|
8
|
Abstract
We revisit the notion of climate, along with its historical evolution, tracing the origin of the modern concerns about climate. The notion (and the scientific term) of climate was established during the Greek antiquity in a geographical context and it acquired its statistical content (average weather) in modern times after meteorological measurements had become common. Yet the modern definitions of climate are seriously affected by the wrong perception of the previous two centuries that climate should regularly be constant, unless an external agent acts upon it. Therefore, we attempt to give a more rigorous definition of climate, consistent with the modern body of stochastics. We illustrate the definition by real-world data, which also exemplify the large climatic variability. Given this variability, the term “climate change” turns out to be scientifically unjustified. Specifically, it is a pleonasm as climate, like weather, has been ever-changing. Indeed, a historical investigation reveals that the aim in using that term is not scientific but political. Within the political aims, water issues have been greatly promoted by projecting future catastrophes while reversing true roles and causality directions. For this reason, we provide arguments that water is the main element that drives climate, and not the opposite.
Collapse
|