Response of Growth and CO2 Uptake of Spring Wheat and Faba Bean to CO2 Concentration under Semifield Conditions: Comparing Results of Field Experiments and Simulations

Dijkstra, Paul; Nonhebel, Sanderine; Grashoff, C.; Goudriaan, J.; Geijn, S.C. van de

doi:10.1016/b978-012505295-5/50015-x

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…The outcome of this study confirmed the notion that species growing early in the season are expected to benefit less from CO 2 doubling than species that grow later and under higher temperatures (Dijkstra et al . 1996).…”

Section: Discussionsupporting

confidence: 85%

“…Higher numbers of ears and seeds per ear contributed to the increased yield (Wheeler et al . 1996a and this study for winter wheat; Dijkstra et al . 1996 and Mulholland et al .…”

Section: Discussionmentioning

confidence: 48%

“…Recent studies have emphasized the interaction between CO 2 and the phenology of the crop (Newton et al . 1994; Dijkstra et al . 1996; Grashoff et al .…”

Section: Introductionmentioning

confidence: 99%

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Seasonal changes in the response of winter wheat to elevated atmospheric CO₂ concentration grown in Open‐Top Chambers and field tracking enclosures

Dijkstra¹,

Schapendonk²,

Groenwold³

et al. 1999

Global Change Biology

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Summary Winter wheat was grown at ambient and elevated (ambient plus 350 μL L–1) CO2 concentrations in open top chambers and in field‐tracking sun‐lit climatized enclosures (elevated is 718 μL L–1). There was no significant effect of CO2 concentration on sheath, leaf and root biomass and leaf area in the early spring (January to April). 24‐h canopy CO2 exchange rate (CCER) was not significantly affected either. However, elevated CO2 concentration increased CCER at midday, decreased evapotranspiration rate and increased instantaneous water‐use‐efficiency during early spring. Leaf, sheath and root nitrogen concentration per unit dry weight decreased and nonstructural carbohydrate concentration increased under elevated CO2, and N‐uptake per unit ground area decreased significantly (– 22%) towards the end of this period. These results contrast with results from the final harvest, when grain yield and biomass were increased by 19% under elevated CO2. N concentration per dry weight was reduced by 5%, but N‐uptake per unit ground area was significantly higher (+ 11%) for the elevated CO2 treatment. 24‐h and midday‐CCER increased significantly more in late spring (period of 21 April to 30 May) (respectively by + 40% and 53%) than in the early spring (respectively 5% and 19%) in response to elevated CO2. Midday evapotranspiration rate was reduced less by elevated CO2 in the late spring (– 13%) than in early spring (– 21%). The CO2 response of midday and 24‐h CCER decreased again (+ 27% and + 23% resp.) towards the end of the growing season. We conclude that the low response to CO2 concentration during the early spring was associated with a growth‐restriction, caused by low temperature and irradiance levels. The reduction of nitrogen concentration, the increase of nonstructural carbohydrate, and the lower evapotranspiration indicated that CO2 did have an effect towards the end of early spring, but not on biomass accumulation. Regression analysis showed that both irradiance and temperature affected the response to CO2.

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Section: Discussionsupporting

confidence: 85%

“…Higher numbers of ears and seeds per ear contributed to the increased yield (Wheeler et al . 1996a and this study for winter wheat; Dijkstra et al . 1996 and Mulholland et al .…”

Section: Discussionmentioning

confidence: 48%

See 1 more Smart Citation

Seasonal changes in the response of winter wheat to elevated atmospheric CO₂ concentration grown in Open‐Top Chambers and field tracking enclosures

Dijkstra¹,

Schapendonk²,

Groenwold³

et al. 1999

Global Change Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results were evaluated to select the best model to predict CO2 emission reduction. The model is validated by comparing the simulated values with the observed value obtained from a detailed onsite audit [16]. The model represents the relationship between the predictable dependent variables (Y) and a set of independent variables (X).…”

Section: Methodsmentioning

confidence: 99%

Carbon Emission Reduction Strategies Through Cleaner Production at Coconut Milk Processing Plant

Isa

Najamuddin

2021

ARFMTS

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The manufacturing industry is one of the most influential sectors contributing to greenhouse gas emissions. As the manufacturing industry strives to achieve its profit goal, most of them face various circumstances to control the rising carbon emissions from the energy, raw material consumption, and waste generations due to production activities. Therefore, it is difficult to quantify the amount of carbon emission reduction if the adjustment is not established according to the manufacturing output. This research concentrates on evaluating energy consumption and waste generation using a statistical approach by a coconut milk processing plant. This research aims to estimate the reduction of greenhouse gas emissions, mainly carbon dioxide (CO2). The baseline models of energy consumptions and waste generations were constructed using single and multiple linear regression methods. Besides, it investigates the performance of ultimate models of electrical consumption, water consumption, fuel consumption, solid waste generation, and wastewater generations using statistical analysis that involves coefficient of correlation, coefficient of determination, analysis of variance (ANOVA), etc. It indicates that with the implementation of the cleaner production (CP) strategy, the plant had reduced 10,474.94 tons of CO2 and 2,579.67 tons of CO2 in 2018 and 2019, respectively. This study is an aid to the management and engineers of the industry to investigate their accomplishment in reducing environmental impacts caused by production activities from any implementation made such as CP and green industry practices.

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“…Two of them are yet to be validated with the industry and the others have no clear information about their validation. Model validation is very key for every model and has to do with comparing model outputs to independent real-world observations so to judge the quantitative and qualitative correspondence with reality [27]. Model validation helps to track known model limitations and identify any new potential shortcomings.…”

Section: Gaps That Prevent Current Manufacturing Mms From Providing Full Evaluationmentioning

confidence: 99%

A Critical Review of Smart Manufacturing & Industry 4.0 Maturity Models: Applicability in the O&G Upstream Industry

Onyeme

Liyanage

2021

Advances in Transdisciplinary Engineering

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The shift towards Industry 4.0 is a fundamental driver of improved changes observed in today’s business organizations. The difficulties in adapting to this new approach pose challenges for many companies especially in the oil and gas (O&G) upstream sector. To make this path much feasible for companies in this industry, Maturity Models (MMs) are very useful tools in achieving this following their use in evaluation of the initial state of a company for planned development journey towards Industry 4.0 (I4.0) readiness and implementation. Study shows that only a limited number of O&G specific roadmaps, MMs, frameworks and readiness assessments are available today. This paper aims to review the currently available Industry 4.0 MMs for manufacturing industries and analyze their applicability in the O&G upstream sector using the systematic literature review (SLR) methodology, recognizing the specific requirements of this industry. The study looks at the key characteristic for O&G sector in relation to the manufacturing sector and identifies research gaps needed to be addressed to successfully support the O&G sector in readiness for Industry 4.0 implementation. An Industry 4.0 maturity model that reflects the industrial realities for the O&G upstream sector more accurately from insights drawn from the reviews of existing MMs is proposed. This reduces the challenges of the transition process towards Industry 4.0 and provides support for the critical change desired for improved efficiency in the sector.

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Response of Growth and CO2 Uptake of Spring Wheat and Faba Bean to CO2 Concentration under Semifield Conditions: Comparing Results of Field Experiments and Simulations

Cited by 6 publications

References 25 publications

Seasonal changes in the response of winter wheat to elevated atmospheric CO₂ concentration grown in Open‐Top Chambers and field tracking enclosures

Seasonal changes in the response of winter wheat to elevated atmospheric CO₂ concentration grown in Open‐Top Chambers and field tracking enclosures

Carbon Emission Reduction Strategies Through Cleaner Production at Coconut Milk Processing Plant

A Critical Review of Smart Manufacturing & Industry 4.0 Maturity Models: Applicability in the O&G Upstream Industry

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Response of Growth and CO2 Uptake of Spring Wheat and Faba Bean to CO2 Concentration under Semifield Conditions: Comparing Results of Field Experiments and Simulations

Cited by 6 publications

References 25 publications

Seasonal changes in the response of winter wheat to elevated atmospheric CO2 concentration grown in Open‐Top Chambers and field tracking enclosures

Seasonal changes in the response of winter wheat to elevated atmospheric CO2 concentration grown in Open‐Top Chambers and field tracking enclosures

Carbon Emission Reduction Strategies Through Cleaner Production at Coconut Milk Processing Plant

A Critical Review of Smart Manufacturing & Industry 4.0 Maturity Models: Applicability in the O&G Upstream Industry

Contact Info

Product

Resources

About

Seasonal changes in the response of winter wheat to elevated atmospheric CO₂ concentration grown in Open‐Top Chambers and field tracking enclosures

Seasonal changes in the response of winter wheat to elevated atmospheric CO₂ concentration grown in Open‐Top Chambers and field tracking enclosures