Article Type : Research Article
Authors : Emilia V, Mihaela RB and Daniela M
Keywords : Sunflower production; Mathematical modelling; Statistical adjustment methods
Sunflower is one of the most important oil plants
grown in the world, about 13% of world oil production is based on this crop, it
is also the most important oil plant in Romania. The importance of sunflower
cultivation is given by its wide use in human nutrition, but also in animal
feed, including industrial and energy uses. In Romania, sunflower was
introduced for oil production in the mid-nineteenth century in Moldova, being
the main plant producing edible oil, currently Romania occupying a leading
place in the European Union, but and in the world to the production of this
plant. In 1990, Romania cultivated 394741 ha with sunflower. The areas
cultivated with this plant registered significant increases, reaching that in
2019 the surface to triple, reaching 1282697 ha, the figure of 1 million
hectares being exceeded for the first time in 2012. The annual production
increased from 556242 t in 1990, to 3569150 t in 2019. So, while the area
cultivated with sunflower increased 3.2 times, the annual production of
sunflower increased 6.4 times. The paper "Analysis of sunflower culture in
Romania during the last 3 decades" presents a mathematical analysis, to
reveal the trend (trend) of production evolution by using statistical
adjustment methods and tests to verify the hypotheses regarding the objective
form of evolution on the period considered in the study, as well as the
comparative analysis of the correlated data by determining the corresponding
advance coefficients. Some significant characteristics are taken into account
in terms of geographical area, soil type, weather conditions, treatments
performed, crop rotation, etc [1].
Sunflower is an oily
plant of great economic and food importance. Due to the content of seeds in
fatty substances (33-56%) and the special quality of the oil resulting in
extraction, the plant is one of the main sources of vegetable fats, used in
human nutrition, respectively the most important source of oil for Romania.
Occupying the fourth place in the world, after soybeans, oil and rapeseed palm
and the first place in Romania, the sunflower is considered a very important
plant for the practice of a sustainable agriculture and for ensuring the food
security and safety of the population. The high nutritional value of sunflower
oil is due to the rich content of unsaturated fatty acids, mainly represented
by linoleic acid (44-75%) and oleic acid (14-43%), as well as the low presence
of linylene acid (0, 2%), components that give it stability and long storage
capacity, superior to other vegetable oils. The nutritional function of
sunflower oil is enhanced by the presence of provitamins of fat-soluble
vitamins A, D, E, phosphatides as well as vitamins B, B, K. The oil also
contains sterols (approximately 0.04%) and tocopherols (antioxidant fraction of
vegetable oil, about 0.07%). The energy capacity (8.8 calories / g oil) and the
high degree of assimilation, place the sunflower oil close to the nutritional
level of butter [2-5]. Refined sunflower oil is mainly used in food, in the
margarine and canning industry. The main use of sunflower is for the oil
industry. Sunflower oil is excellent for nutrition, with pleasant fluidity,
colour, taste and smell. The product is also used in industry for the
production of special varnishes and resins, as well as in painting. The
residues resulting from the refining process are used in the manufacture of
soaps, in the production of waxes, phosphatides, lecithin and tocopherols.
Phosphatides and lecithin extracted from sunflower oil are used in the food
industry, bakery, pastry, in the preparation of chocolate and sausages. This
sector has seen an upward trend, mainly due to the constant increase in
sunflower production and the constant demand for crude sunflower oil on the
foreign market, of 100 thousand tons per year. In the oil industry, the number
of companies has increased, currently operating a number of 548 production units,
of which 17 units are high capacity and process over 90% of the raw material.
The utilization index of the production capacity in 2018 was: 61.5%. In 2017,
the refurbishment and investments in the oil industry amounted to 48 billion
lei and consisted mainly in refurbishing the operations of extraction,
vinification, lecithinization and refining, as well as in the procurement of
technologies and installations for the production of emulsifiers with high
content of monoglycerides, seedling plants. Cakes resulting from the oil
extraction process (approximately 300 kg / t seed), are a valuable source of
protein for ruminants, rabbits, pigs and birds [5,6]. Cakes contain crude
protein (between 33.7 and 47.8%) and essential amino acids, close to those of
soybeans, with the exception of lysine, which is found in smaller amounts
[7-10]. The energy value of the cakes is correlated with the degree of peeling
of the seeds. The seeds, less rich in oil, are used directly for consumption,
whole or peeled, as well as for halva. The stems can be used as a heat source
(locally), for the manufacture of soundproofing plates or for obtaining calcium
carbonate. The sunflower is also appreciated as a fodder plant, being
cultivated mainly for silage. Sunflower is also an excellent honey plant. From
one hectare of sunflower can be obtained a quantity of 30 to 130 kg of honey
[11-18]. Through the organic residues left after harvest, the sunflower returns
to the soil appreciable quantities of mineral elements and organic matter,
estimated in the case of a production of 3500 kg / ha, at 65 kg N, 30 kg PO,
300 kg KO and about 7 tons of dry matter, the equivalent of 1200-1500 kg of
humus [19-21]. Sunflower can also have medicinal uses. From the ligulate
flowers (which contain quercitrin, anticyanin, choline, betaine, xanthophyll,
etc.), an alcoholic extract is obtained which is used in the fight against
malaria, and the tincture in lung diseases. From achenes, given the content in
phytin, lecithin, cholesterol, products were prepared indicated in the
prophylaxis of dysentery, typhoid fever and for the healing of suppurative
wounds. The oil is used (in folk medicine) to macerate plants used to treat
wounds and burns. Sunflower is not a very demanding plant, the expenses with
this crop are too high for us: nitrogen and phosphorus fertilization is
moderate, the requirements are high compared to potassium, but we have abundant
refunds here, the costs for seed are comparable to those of corn. Also,
sunflower adapts better than corn, on lands with medium quality soils and
better withstands water stress. At the same time, the calendar of agricultural
works such as land preparation, sowing, chemical weed control, harvesting can
be done without hindering the works intended for other agricultural crops. There
are also a number of inconveniences of the flower - sun that impose very
serious restrictions on rotation, excluding monoculture and return to the same
land earlier than 6 years, susceptibility to disease, difficulties in placement
after many plants with common pests and diseases, high consumption of water and
nutrients from the soil, which requires fertilization of post-market crops by
applying high doses of fertilizers. In 2019, Romania ranked first in the EU
both in terms of sunflower production and cultivated area. The spectacular
increase in sunflower cultivation in recent years is due to the possibility for
growers to establish the structure of crops according to the market, the
involvement of oil factories in cultivation and subsidization, greater
stability of sunflower production, due to greater tolerance of its drought.
Mathematical
Analysis, To Identify the Trend of the Evolution of Sunflower Production
The analysis and
calculations were performed for the sunflower crop, but can also be extended to
the main vegetable crops: grain cereals, grain legumes, oil plants, beets,
medicinal and aromatic plants, tobacco, fodder, etc.
Study
period - 30 years between 1990-2020
Primary data were considered regarding the
cultivated land areas (hectares) and the realized production (tons).
A primary statistical
analysis on the sequence of the period 1990-2021, reveals a good direct
correlation, worth 0.73 for the considered dynamic series: cultivated area and
production, which leads to the conclusion of the possibility of a comparative
study and, respectively, to appropriate mathematical models. The statistical
analysis of the dynamic series is based on a system of indicators, which
characterizes both the quantitative relationships within the series and the
entire time period to which the data refer. Thus, the statistical analysis
performed in this study is performed by calculating descriptive statistical
indicators.
As a general method, the
research uses the statistical apparatus applied dynamic data series consisting
of annual production per ha (t / ha) over the last three decades and its
statistical analysis using the method of indicators and statistical indices. Although
the homogeneity of the dynamic series is difficult to ensure, for the
homogeneity analysis a single calculation and evaluation procedure was used,
ensuring the delimitation on qualitatively different stages in the evolution of
the phenomenon of indicators studied in dynamics, keeping the same length of
time intervals and maintaining the periodicity. The series is formed. The
length of the considered series and the collection of the studied data meet the
condition of large numbers, being in a sufficient number of data for the
horizon of statistical analysis that correctly substantiates the performed
calculations. These characteristics give continuity to the data in terms of
time variables and, on the other hand, give the possibility to model the dynamic
series with an analytical function. Of time this is the main reason why the
establishment of the unit of time to which the chronological series analysis
refers is made in relation to the declared purpose of the research, of the
content and of the possibilities of quantification of each indicator.
The third part of the
study highlights
·
average
indicators calculated from absolute values 0.7938 t and 0.02, respectively from
relative values: the average annual index obtained in the amount of 100.98% and
the average growth rate, resulting in the value of 0.98%
·
The
average dynamic (growth) index resulting in 1.01
The resulting chronological average of 1.51 t / ha.
Figure 1: Authors calculations, according to the National Institute of Statistics, TEMPO-on-line basis.
Table 1:
The first part of the study calculates the crop production per hectare and the
corresponding graphical representation.
Year |
Area cultivated (ha) |
Annual production (t) |
Annual production (t/ha) |
1990 |
394741 |
556242 |
1.4 |
1991 |
476848 |
611956 |
1.28 |
1992 |
615050 |
773986 |
1.25 |
1993 |
588367 |
695833 |
1.18 |
1994 |
582192 |
763697 |
1.31 |
1995 |
714490 |
932932 |
1.3 |
1996 |
916784 |
1095596 |
1.19 |
1997 |
780746 |
858060 |
1.09 |
1998 |
962150 |
1073316 |
1.11 |
1999 |
1043011 |
1300929 |
1.24 |
2000 |
876807 |
720871 |
0.82 |
2001 |
800282 |
823549 |
1.02 |
2002 |
906219 |
1002813 |
1.1 |
2003 |
1188037 |
1506398 |
1.26 |
2004 |
976960 |
1557813 |
1.59 |
2005 |
970950 |
1340940 |
1.38 |
2006 |
991363 |
1526232 |
1.53 |
2007 |
835923 |
546922 |
0.65 |
2008 |
813891 |
1169936 |
1.43 |
2009 |
766080 |
1098047 |
1.43 |
2010 |
790814 |
1262926 |
1.59 |
2011 |
994984 |
1789326 |
1.79 |
2012 |
1067045 |
1398203 |
1.31 |
2013 |
1074583 |
2142087 |
1.99 |
2014 |
1001020 |
2189309 |
2.18 |
2015 |
1011527 |
1785771 |
1.76 |
2016 |
1039823 |
2032340 |
1.95 |
2017 |
998415 |
2912743 |
2.91 |
2018 |
1006994 |
3062690 |
3.04 |
2019 |
1282697 |
3569150 |
2.78 |
2020 |
1170372 |
2204312 |
1.88 |
Interpretation
of values of dynamics indicators
The results of the calculations reveal a very stationary almost easily favourable evolution of the sunflower production characterized by the following combination of indicators: supraunitary dynamics index, dynamics rhythm and absolute positive change.
For the considered
dynamic series, it is recommended to use the average indicators as a way of
presentation for the evolution of the corresponding qualitative characteristics.
·
The
values of the average indicators resulting from the analysis reveal a slightly
increasing evolution that requires the taking of special measures for a short,
medium or longer period of time to increase the production qualitatively and quantitatively.
·
The
results obtained from the analysis reflect a weakly favourable situation in the
evolution of sunflower production during the research period reflected in the
following combination of indicators: super unit dynamics index, dynamics rhythm
and positive absolute change, but almost insignificant in value.
·
The
evolution of sunflower production in the researched period can be grouped into
3 sub periods, quantitatively characterized as follows:
·
The
first decade, 1990-2000, except for the years 1994, 1998 and 1999, saw a slight
decrease in production
·
The
second decade, 2001-2011, except for the years 2005 and 2007 reveals a slight
increase in annual production
·
The
last decade, 2012-2020 shows a relative instability, alternating as evolution.
The last 2 years, respectively 2019 and 2020 show a slight decrease in sunflower production.
Table 2: The second part of the study calculates the absolute, relative and average indicators, both with a fixed and a mobile base.
Year |
Absolute indicators |
Relative indicators (%) |
|||||
Level |
Absolute
changes |
Dynamic
indices |
Growth
rate |
||||
Annual
production (t/ha) |
with fixed
base |
with
mobile base |
with fixed
base |
with
mobile base |
with fixed
base |
with
mobile base |
|
1990 |
1.4 |
0 |
- |
1.0000 |
- |
0 |
- |
1991 |
1.28 |
-0.12 |
-0.12 |
0.9143 |
0.91 |
-0.0857 |
-0.0857 |
1992 |
1.25 |
-0.15 |
-0.03 |
0.8929 |
0.98 |
-0.1071 |
-0.0234 |
1993 |
1.18 |
-0.22 |
-0.07 |
0.8429 |
0.94 |
-0.1571 |
-0.0560 |
1994 |
1.31 |
-0.09 |
0.13 |
0.9357 |
1.11 |
-0.0643 |
0.1102 |
1995 |
1.3 |
-0.1 |
-0.01 |
0.9286 |
0.99 |
-0.0714 |
-0.0076 |
1996 |
1.19 |
-0.21 |
-0.11 |
0.8500 |
0.92 |
-0.1500 |
-0.0846 |
1997 |
1.09 |
-0.31 |
-0.1 |
0.7786 |
0.92 |
-0.2214 |
-0.0840 |
1998 |
1.11 |
-0.29 |
0.02 |
0.7929 |
1.02 |
-0.2071 |
0.0183 |
1999 |
1.24 |
-0.16 |
0.13 |
0.8857 |
1.12 |
-0.1143 |
0.1171 |
2000 |
0.82 |
-0.58 |
-0.42 |
0.5857 |
0.66 |
-0.4143 |
-0.3387 |
2001 |
1.02 |
-0.38 |
0.2 |
0.7286 |
1.24 |
-0.2714 |
0.2439 |
2002 |
1.1 |
-0.3 |
0.08 |
0.7857 |
1.08 |
-0.2143 |
0.0784 |
2003 |
1.26 |
-0.14 |
0.16 |
0.9000 |
1.15 |
-0.1000 |
0.1455 |
2004 |
1.59 |
0.19 |
0.33 |
1.1357 |
1.26 |
0.1357 |
0.2619 |
2005 |
1.38 |
-0.02 |
-0.21 |
0.9857 |
0.87 |
-0.0143 |
-0.1321 |
2006 |
1.53 |
0.13 |
0.15 |
1.0929 |
1.11 |
0.0929 |
0.1087 |
2007 |
0.65 |
-0.75 |
-0.88 |
0.4643 |
0.42 |
-0.5357 |
-0.5752 |
2008 |
1.43 |
0.03 |
0.78 |
1.0214 |
2.20 |
0.0214 |
1.2000 |
2009 |
1.43 |
0.03 |
0 |
1.0214 |
1.00 |
0.0214 |
0.0000 |
2010 |
1.59 |
0.19 |
0.16 |
1.1357 |
1.11 |
0.1357 |
0.1119 |
2011 |
1.79 |
0.39 |
0.2 |
1.2786 |
1.13 |
0.2786 |
0.1258 |
2012 |
1.31 |
-0.09 |
-0.48 |
0.9357 |
0.73 |
-0.0643 |
-0.2682 |
2013 |
1.99 |
0.59 |
0.68 |
1.4214 |
1.52 |
0.4214 |
0.5191 |
2014 |
2.18 |
0.78 |
0.19 |
1.5571 |
1.10 |
0.5571 |
0.0955 |
2015 |
1.76 |
0.36 |
-0.42 |
1.2571 |
0.81 |
0.2571 |
-0.1927 |
2016 |
1.95 |
0.55 |
0.19 |
1.3929 |
1.11 |
0.3929 |
0.1080 |
2017 |
2.91 |
1.51 |
0.96 |
2.0786 |
1.49 |
1.0786 |
0.4923 |
2018 |
3.04 |
1.64 |
0.13 |
2.1714 |
1.04 |
1.1714 |
0.0447 |
2019 |
2.78 |
1.38 |
-0.26 |
1.9857 |
0.91 |
0.9857 |
-0.0855 |
2020 |
1.88 |
0.48 |
-0.9 |
1.3429 |
0.68 |
0.3429 |
-0.3237 |
Source: Authors calculations, according to the
National Institute of Statistics, TEMPO-on-line basis |
Project
development proposals through
·
Extending
the study to the main agricultural crops.
·
Carrying
out the analysis to highlight particularities arranged by geographical areas.
·
Construction
of a mathematical model to reveal the trend (trend) of production evolution by
using statistical adjustment methods and tests to verify the hypotheses
regarding the objective form of evolution during the period considered in the
study.
·
Linear
and quadratic mathematical models can be used to generate the trend function
through a one-factor model and to determine the estimators and errors so that
the optimal model can be chosen.
·
Extending
the analysis of the properties of the dynamic series from the point of view of
the characterization of the characteristics under the aspect:
·
Variability,
·
Homogeneity,
·
Comparability
·
Comparative
analysis of correlated data by determining the corresponding advance
coefficients.
Consideration of significant characteristics in
terms of geographical area, soil type, weather conditions, and treatments
performed, crop rotation, etc.
1.
Alecu
I, Colab SI. Management in Agricultura. Ed Ceres Bucuresti. 2017.
2.
Robert
B. Economie Rurale, Librairie Armand Colin. 1971.
3.
Mariana
B, Mihaela RB. Youth and the labour market in rural areas, Sesiunea stiintifica
internationala: Cercetari de Economie Agrara si Dezvoltare Rurala. Agricultural
Econ Rural Development. 2022.
4.
Berca
M. Ingineria ?i managementul resurselor pentru dezvoltare rurala. Ed Ceres
Bucuresti. 2003.
5.
Dona
I. Note de curs. USAMV-Bucuresti.
6.
Dona
I. Politici agricole. Edition Semne. 2020.
11.
Otiman
PI. Dezvoltarea rurala in Romania. Edition Agroprint, Timisoara. 2017.
12.
Otiman
PI. Restructurarea agriculturii si dezvoltarea rurala a Romaniei in vederea
aderarii la. Agroprint, Timisoara. 2018.
13.
Popescu
G. Probleme de politica agrara. Edition ASE. 2001.
14.
Zahiu
L. Olitici si piete agricole. Edition Ceres, Bucuresti. 2005.
15.
OECD.
The World Commission on Environment and Development, Paris. 1987.
16.
Recensamantul
General Agricol, Rezultate preliminare, iunie. 2003.
17.
Studii
si cercetari economice, Centrul de Informare si Documentare Economica.
Bucuresti. 1996-2017;
18.
La
Charte europeenne de l’espace rural – UN cadre politique pour le developpement
rural, Strasbourg. 2015.
19.
SAPARD
– le Programme Special de Preadhesion pour l’Agriculture et le Developpement
Rural, DG Agriculture. 2020.
20.
Programul
Na?ional pentru agricultura ?i dezvoltare rurala al Romaniei.
Institutul Na?ional de Statistic?, Baza
TEMPOon-line.