Energy use and carbon emission of conventional and organic sugar beet farming

Šarauskis, Egidijus; Romaneckas, Kęstutis; Kumhála, F.; Kriaučiūnienė, Zita

doi:10.1016/j.jclepro.2018.08.077

Cited by 30 publications

(11 citation statements)

References 34 publications

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“…Given that the production of organic sugar is projected to expand, more knowledge about growing organic sugar beet is needed. The effects of different agrotechnologies on soil properties, and the productivity and quality of sugar beet root-crop have been widely investigated in Lithuania [29][30][31][32][33] and worldwide [34]. However, the practices of sugar beet intercropping under organic farming conditions have not been sufficiently investigated and there is a need for more research.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Intercropping on Soil Fertility and Sugar Beet Productivity

et al. 2020

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There is a lack of research on the practice of intercropping sugar beet and the impact of such agrocenoses on soil and crop fertility, especially under organic farming conditions. For this reason, a three-year stationary field experiment was performed at Vytautas Magnus University, Agriculture Academy, Lithuania. Sugar beet was grown continuously with intercropped Persian clover (Trifolium resupinatum L., MC), white mustard (Sinapis alba L., MM) and spring barley (Hordeum vulgare L., MB) as a living mulch. Inter-row loosening (CT) and mulching with ambient weeds (MW) were used as comparative treatments. The results showed that, under minimal fertilization, CT and intercropping increased the average content of nitrogen, phosphorus and potassium in the soil. However, the average content of magnesium was reduced in single cases (MW, MB), and the average content of sulphur was reduced in all cases. Intercropping significantly decreased the yields of sugar beet root-crop, but was mainly neutral in quality terms. The meteorological conditions during experimentation had a weak impact on root-crop quantity and quality. Generally, the practice of sugar beet intercropping requires more detailed research on how to minimize the competition between the sugar beet, living mulch and weeds, and how to balance the nutrition conditions.

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Section: Introductionmentioning

confidence: 99%

The Impact of Intercropping on Soil Fertility and Sugar Beet Productivity

et al. 2020

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“…In this study, 1 ha land of tea was considered as functional unit (FU). Land-based FU is common in agricultural energy flows modeling studies [13, [63][64][65][66][67][68][69][70]. The energy associated with each input was estimated by multiplying the amount of consumed input by an energy coefficient [71,72].…”

Section: Agricultural Case Studymentioning

confidence: 99%

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Nikkhah

Rohani

Rosentrater

et al. 2019

Env Prog and Sustain Energy

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There are some studies regarding the prediction of agricultural energy flows using artificial neural networks (ANNs). These models are quite sensitive to correlations amongst inputs, and, there are often strong correlations amongst energy inputs for agricultural systems. One potential method to remediate this problem is to use principal component analysis (PCA). Therefore, the purpose of this research was to predict energy flows for a specific agricultural system (Iranian tea production) via a novel methodology based on ANNs, and using principal components as model inputs, not raw data. PCA results showed that the first and second components could account for more than 99% of variation in the data, thus the dimensions of the data set could be decreased from six to two for the prediction of energy flows for Iranian tea production. Using these principal components as inputs, an ANN model with 2–15–1 structure was determined to be optimal for energy flow modeling of this system. To conclude, the results of this study highlighted that the use of PC as ANN inputs improved ANN model prediction through reducing its complexity and eliminating data colinearity. Many agricultural systems could benefit from using this methodology for energy modeling. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13130, 2019

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“…There exists an undeniable nexus between the agricultural (food or otherwise) and energy sectors, because while the former is an energy-user, it also doubles as a provider of bio-energy [1]. Over the years, owing to a multiplicity of causative factors-a limited supply of arable agricultural land, technological changes, population growth, and thereby a rise in the demand for agricultural outputs-the energy usage of the agricultural sector globally has tended to increase [2,3]. To ensure the sustainable development of the agricultural sector, it is thereby indispensable to reduce the reliance on fossil fuels (directly or indirectly), decrease the pollution of the environmental media (air, water, and soil), and achieve economic feasibility.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A Cluster Analysis on the Energy Use Indicators and Carbon Footprint of Irrigated Wheat Cropping Systems

et al. 2022

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The objective of this study is to analyze the energy use efficiency and carbon footprint of irrigated wheat systems in different Iranian provinces. The authors resort to the k-means clustering technique to fulfil the said objective. The empirical results reveal that the average total input energy (59.5 GJ ha−1) is higher than the average energy output (45.82 GJ ha−1) from wheat production, resulting in an average energy efficiency of 0.77, thus rendering the production of irrigated wheat in Iran energy-inefficient on average. Among the thirty wheat-producing Iranian provinces considered in this analysis, only six—East Azerbaijan, Golestan, Ardabil, Kohgiluyeh and Boyer-Ahmad, Alborz, and West Azerbaijan—register an energy use efficiency greater than unity. The average total of greenhouse gas (GHG) emissions from irrigated wheat is 2243.54 kg CO2-eq ha−1 (with electricity and diesel fuel contributing 52.4% and 29.4%, respectively). The authors categorize the clusters into five groups ranging from sustainable to unsustainable. Five of the six provinces referred to earlier fall into the ‘sustainable’ category, with Bushehr being the sixth. The wheat production units in the ‘sustainable’ category can serve as a benchmark for the clusters in the other categories, which can move up the ladder of sustainability. The authors also recommend measures that policymakers can undertake to ensure the sustainable development of wheat production in Iran, fulfilling the social imperative of food self-sufficiency while truncating the environmental footprint and ensuring economic feasibility.

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Energy use and carbon emission of conventional and organic sugar beet farming

Cited by 30 publications

References 34 publications

The Impact of Intercropping on Soil Fertility and Sugar Beet Productivity

The Impact of Intercropping on Soil Fertility and Sugar Beet Productivity

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

A Cluster Analysis on the Energy Use Indicators and Carbon Footprint of Irrigated Wheat Cropping Systems

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