Chapter 16 - Sunflower
Abstract
Sunflower (Helianthus annuus L.) is an annual oilseed crop primarily grown for its edible oil and fruits in temperate and subtropical climates worldwide. Its oil is somewhat superior to other vegetable oils due to the greater proportion of the unsaturated fatty acids and the content of bioactive compounds (e.g. tocopherols and phytosterols). Here, we update the knowledge about sunflower crop physiology in the context of climate change with focus on (i) the main factors affecting growth and phenology, (ii) the capture and efficiency use of radiation, water and nutrients, iii) how grain number (GN) and grain weight are defined to conform the crop yield and iv) how grain and oil quality are elaborated. All these traits are analysed considering the genetic potential of the crop and how crop management practices can modulate them (by affecting environmental factors).
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Carbon footprint of hemp and sunflower oil in southern Italy: A case study
2024, Ecological IndicatorsThe proteoleaginous plants demand has seen significant growth, leading to an expansion of the sunflower (Helianthus annuus, L.) and industrial hemp (Cannabis sativa, L.) cultivation area in Italy. However, by-products obtained during seed oil extraction and agricultural residues are often unused due to the absence of a receptive market and nearby processing centers. The carbon footprint (CF) methodology was used to compare the two supply chains considering the soil incorporation of all crop residues and by-products. The boundary of the supply chains analyzed includes all the agricultural processes that occur during cultivation and the subsequent oil extraction phase. Furthermore, research explored the direct and indirect environmental benefits of incorporating by-products into the soil, in terms of reducing the need for mineral fertilizers to restore soil fertility due to the nutrients contained in the buried biomass, and the potential carbon sequestration achievable. Results show that 1 kg of sunflower and hemp oil release 4.49 kg CO2-eq and 23.34 kg CO2-eq, respectively. Agriculture represents the most impacting phase and, in particular, fertilization, tillage and harvest are responsible for high emissions. The different results between the two supply chains can be attributed mainly to yield and extraction efficiency. The use of by-products as amended in the soil (avoided fertilizers) contributes to a reduction of greenhouse gas (GHG) emissions by −0.53 kg CO2-eq and −7.87 kg CO2-eq per kg of sunflower and hemp oils, respectively. Additionally, the sequestration of carbon in biomass can result in a further reduction of −1.16 and −33.6 kg CO2-eq per kg of sunflower and hemp oil, respectively. In summary, sunflower oil production emits 74 % less CO2 than hemp oil. However, if all crop biomass is buried, hemp has the potential to be more sustainable. This phenomenon depends on many factors such as soil type, climate, and farming practices. The study outcomes can aid policymakers, farmers, and the agribusiness to make informed decisions on promoting and expanding sustainable sunflower and hemp cultivation in Italy.
Electrospun plant protein-based nanofibers in food packaging
2024, Food ChemistryElectrospinning is a relatively simple technology capable to produce nano- and micron-scale fibers with different properties depending on the electrospinning conditions. This review critically investigates the fabrication of electrospun plant protein nanofibers (EPPNFs) that can be used in food and food packaging applications. Recent progress in the development and optimization of electrospinning techniques for production of EPPNFs is discussed. Finally, current challenges to the implementation of EPPNFs in food and food packaging applications are highlighted, including potential safety and scalability issues. The production of plant protein nanofibers and microfibers is likely to increase in the future as many industries wish to replace synthetic materials with more sustainable, renewable, and environmentally friendly biopolymers. It is therefore likely that EPPNFs will find increasing applications in various fields including active food packaging and drug delivery.
Comparison of influential input variables in the deep learning modeling of sunflower grain yields under normal and drought stress conditions
2023, Field Crops ResearchCrop yield prediction is a complex task with nonlinear relationships due to its dependence on multiple factors such as polygenic traits, environmental effects, genetics and environment interactions, etc. These cases make conventional statistical techniques unable to explain the nonlinear and complex relationship between performance and its components.
This research was conducted to estimate sunflower seed yield using multiple linear regression (MLR) and convolutional neural network (CNN). It also investigated the effect of different input variables on deep learning modeling of sunflower seed yield under normal and drought stress (DS) conditions.
The 100 pure lines of oil seed sunflower were investigated during two crop years in the field under normal and DS conditions in terms of seed yield and morphological traits. The CNN model was implemented using different combinations of input variables. In this regard, all studied parameters were first used as input variables, and then stepwise regression was performed for both conditions. In this step, the input variables for yield modeling with the CNN model consisted of parameters included in the regression model and those common in normal and DS conditions.
The CNN model with two input variables (head diameter [HD] and number of leaves [NL]), which were common in the regression model for both conditions, achieved higher accuracy and performance in predicting sunflower yield under normal conditions (R2 =0.921, MAE=5.425, and RMSE=6.462). Nonetheless, in DS conditions, the CNN model with seven input variables (i.e., leaf width [LW], NL, plant height [PH], days to flowering [DF], stem diameter [SD], petiole length [PL], and HD) demonstrated higher accuracy and performance in predicting sunflower yield (R2 =0.915, MAE=3.632, and RMSE=4.330). The CNN model outperformed the MLR model in both conditions in terms of accuracy and performance. Sensitivity analysis identified LW, NL, and length of leaf [LL] traits as important and influential traits for yield prediction under normal and DS conditions.
The CNN model was successful in reducing the number of variables needed to model sunflower seed yield. With the important parameters identified, sunflower yield can be predicted with higher accuracy, lower cost, and in a shorter time, even if other parameters are not available, in both normal and drought-prone conditions.
The CNN model can potentially be used as a promising tool for predicting sunflower yield in yield increase programs under different growing conditions.
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