ISSN 2410-5708 / e-ISSN 2313-7215
Year 9 | No. 24 | p. 162-176 | February - May 2020
© Copyright (2020). National Autonomous University of Nicaragua, Managua.
This document is under a Creative Commons
Attribution-NonCommercial-NoDerivs 4.0 International licence.
Evaluation of the Sovereignty and Food and Nutrition Security of families in the rural communities of La Concepción municipality, department of Masaya in the period from May to December of the year 2015
https://doi.org/10.5377/torreon.v9i24.9725
Submitted on February 13th, 2020 / Accepted on February 19th, 2020
B.A. Rubén Ismael Rivera Vásquez
Professor
Faculty of Science and Engineering, UNAN-Managua
Ph.D. Manuel Enrique Pedroza Pacheco
Associate Professor
Faculty of Medical Sciences, UNAN-Managua
Keywords: analysis of variance, consumption, Pearson's correlation, income, production
Abstract
To evaluate Food and Nutritional Sovereignty and Security in the rural communities of the Municipality of La Concepción, a descriptive, correlational and analytical, prospective and transversal research was carried out. From the results obtained, it was concluded that: 1) The main socioeconomic characteristics under study: in the heads of households, the male sex stands out with 58.13%, with a low level of schooling with 36.9% in primary education. 90% of them have active employment, although with low incomes of C$ 4,346.38 per month. 2) The main associations between the variables of production, income, and consumption, occur between consumption of basic grains and the number of people in the household, it was demonstrated a significant correlation between the number of people in the household and the consumption of rice, beans, sugar, oil, eggs, onion, potatoes, green banana. 3) A low positive, but significant, the correlation was established between the production and consumption of red beans. 4) The ANOVA between production and income variables showed significant differences. The ANOVA on citrus production for the different communities did not show significant differences. 5) The MANOVA for the consumption pattern carbohydrate showed that consumption between rural communities has significant differences, with a p=0.022. For the protein consumption pattern, the MANOVA showed that consumption between rural communities has no significant differences with a p = 0.209.
Introduction
Nicaragua has been affected by conditions that limit the development of the population, taking into account that the SSAN has a multisectoral and multidimensional approach, the articulation of actors in this area has been notable and important to contribute to the reduction of Food Insecurity and Nutritional (InSAN as in Spanish). Masaya has a chronic malnutrition rate of 13% according to the Nicaraguan Survey of Demography and Health (ENDESA, 2011/2012). On the other hand, Nicaragua has 14.6% of extremely poor, in the rural area 26.6% are extremely poor and in the urban area, they are 63.3% according to the household survey on the measurement of the standard of living 2009 (EMNV, 2009).
The territory of La Concepción is characterized by having high rates of agricultural production and this is linked to income, but adverse factors could be decreasing production and therefore their income causing a rise in the prices of food products and this triggers a diversity of problems, where the most affected population suffers from extreme poverty. This problem that crosses the regions of the said municipality is related to the pillars of Food and Nutrition Sovereignty and Security (availability, access, consumption, quality and food safety and use or biological use of food).
This study focused partially on three pillars of the SSAN, such as the access that families have to basic and nutritious food demanded by the basic basket (23 food products) through their income and production, as well as the availability of these families concerning family income and their own food production, and otherwise their consumption. On the other hand, the study developed the experience of knowing the feeding status of the rural families in which they are located and being able to compare in different rural communities of the same municipality to be able to see where there is greater vulnerability and therefore see the problems that these have, both in production and income.
Materials and methods
The main objective was to “Evaluate Food and Nutrition Sovereignty and Security: Production, Income, and Consumption, of families, in the rural communities of the Municipality of La Concepción, Department of Masaya in the period May-December 2015”. According to the methodological design the type of study is descriptive, according to the method of study is observational (Piura, 2006). According to the classification of Hernández, Fernández and Baptista 2006, it is correlational. According to the time of occurrence of the facts and the registration of the information, it is prospective, due to the period and sequence of the study, it is transversal and according to the analysis and scope of the analytical results of cause-effect (Canales, Alvarado and Pineda, 1996).
The implementation of the probabilistic sampling for the estimation of the population mean was highlighted, using the Simple Random Sampling method, taking as the main variable the number of people in the household, the sample size required to estimate μ with the limit of estimation error B, was 160 households (Scheffer, Mendenhall & Ott, 1986). For the statistical analysis, descriptive and inferential techniques were implemented that included the use of Pearson’s parametric correlation and univariate statistical models, ANOVA and LSD, and multivariate, ACP and MANOVA (Lambda de Wilks).
Results and Discussion
Socio-economic characteristics in the context of the SSAN, of the families in the communities under study.
Based on the estimator of the average number of people in the household, in the piloting of this investigation, it was determined that it is 5 (Table 1), which coincides with the number of persons per house occupied, according to the 2005 census figures (INIDE, 2006). 37% of the heads of households have their last year of primary school approved, the majority of them (51%) being married (Tables 2 and 3).
Table 1. Descriptive statistics of the number of people who make up the household |
|||||
N |
Mínimum |
Máximum |
Mean |
Standard deviation |
|
Number of people at home |
160 |
1 |
11 |
5.06 |
1.9 |
Table 2. Schooling of the household head |
||
Schooling |
Frequency |
Percentage |
Illiterate |
29 |
18.1 |
Elementary |
59 |
36.9 |
Hihgschool |
50 |
31.3 |
University |
22 |
13.8 |
Total |
160 |
100.0 |
Table 3. Marital status of the household head |
||
Frequency |
Percentage |
|
Married |
82 |
51.3 |
De facto Union |
41 |
25.6 |
Single |
30 |
18.8 |
Widowed |
4 |
2.5 |
Divorced |
3 |
1.9 |
Total |
160 |
100.0 |
According to ENDESA 2007, Masaya has an employment rate of 52.9%, however, in the 18 rural communities of this research, an employment rate of 90% of the heads of households with a current active labor situation was confirmed (Table 4).
Table 4. The sector in which the head of household works |
||||
Frequency |
Percentage |
Valid percentage |
||
Valid |
Private |
44 |
27.5 |
30.6 |
Public |
23 |
14.4 |
16.0 |
|
Free-lance |
77 |
48.1 |
53.5 |
|
Total |
144 |
90.0 |
||
Lost |
90.0 |
16 |
10.0 |
|
Total |
160 |
100.0 |
||
Valid percentage: its calculation does not take into account the lost |
||||
Lost: these are data that are not included in the category of the variable |
Correlations between production, income, and consumption, in the context of the SSAN that occurs in the communities under study.
98% of rural families acquire food on their own, and they do not lack food during the three meal times per day (Table 5).
Table 5. The source which supplies the food they consume |
||
Sources by which their food has been supplied |
Counting |
Table n % |
Government |
1 |
0.6% |
Family |
7 |
4.4% |
NGO |
2 |
1.3% |
By their own |
157 |
98.1% |
Table of n %: independent percentage of each category |
In these communities not much meat is consumed, 37% of the population does not consume beef, pork 49% of the population does not consume it. About the consumption of fish meat, 67% of people do not consume it. Poultry meat has the highest consumption, only 33%, it consumes it sometimes (Table 6).
Table 6. Frequency of their family consumes the following foods |
|||||||||||
Never |
Rarely |
Sometimes |
Almost daily |
Dialy |
|||||||
Counting |
% |
Counting |
% |
Counting |
% |
Counting |
% |
Counting |
% |
||
Basic Foods |
Rice |
0 |
0.0% |
0 |
0.0% |
1 |
.6% |
2 |
1.3% |
157 |
98.1% |
Been |
2 |
1.3% |
0 |
0.0% |
10 |
6.3% |
8 |
5.0% |
140 |
87.5% |
|
Sugar |
0 |
0.0% |
0 |
0.0% |
5 |
3.1% |
2 |
1.3% |
153 |
95.6% |
|
Oil |
1 |
.6% |
0 |
0.0% |
3 |
1.9% |
1 |
.6% |
155 |
96.9% |
|
Carnes |
Beef |
59 |
36.9% |
35 |
21.9% |
60 |
37.5% |
5 |
3.1% |
1 |
.6% |
Pork |
78 |
48.8% |
37 |
23.1% |
43 |
26.9% |
2 |
1.3% |
0 |
0.0% |
|
Poultry |
30 |
18.9% |
25 |
15.7% |
53 |
33.3% |
44 |
27.7% |
7 |
4.4% |
|
Fish |
107 |
66.9% |
26 |
16.3% |
22 |
13.8% |
3 |
1.9% |
2 |
1.3% |
In the municipalities under study, 51.3% of the population surveyed are active agricultural producers (Table 7) and of these 81% lack technical training to improve their agricultural production (Figure 1).
Figure 1. Heads of producing households that have been trained.
On average, the household has an income of C$ 6,559 per month, on average 2 people contribute to the household income. However, relating the monthly income of families to the basic food basket, which is made up of 23 products with a value of C$ 8,489.86, according to INIDE April 2016, this reveals that in rural communities they do not reach their income to obtain the basic food basket (Table 7).
Table 7. Descriptive statistics of family income |
|||||
n |
Minimum |
Maximum |
Mean |
Standard deviation |
|
Total monthly income at home C$ |
160 |
500.00 |
108000.00 |
6559.3750 |
9629.32691 |
How many people contribute to the family income |
160 |
1 |
7 |
1.83125 |
1.14497466 |
The consumption of basic grains (rice and beans), sugar, oil, eggs, onions, potatoes, green plantains, and cabbage, has had positive and significant correlations, with respect to the number of members in the household, that is to say, that the need is greater of feeding, rice and beans are still missing in the homes of the rural communities under study (Table 8). The families that most dedicate themselves to the planting of beans, with this they sustain since of the production they consume the beans in their homes (Table 9).
Table 8. Pearson Correlation Matrix between food consumption and the number of people in the household |
||
The average number of people in the household |
Number of people in the household |
|
Rice consumption (lb) |
Pearson correlation |
0.38** |
Sig. (bilateral) |
0.000 |
|
N |
160 |
|
Been (lb) monthly consumption |
Pearson correlation |
0.34** |
Sig. (bilateral) |
0.000 |
|
N |
158 |
|
Sugar (lb) weekly consumption |
Pearson correlation |
0.24** |
Sig. (bilateral) |
0.002 |
|
N |
160 |
|
Oil (lt) weekly consumption |
Pearson correlation |
0.29** |
Sig. (bilateral) |
0.000 |
|
N |
158 |
|
eggs (doz) weekly consumption |
Pearson correlation |
0.17* |
Sig. (bilateral) |
0.04 |
|
N |
133 |
|
White onions (unit) weekly consumption |
Pearson correlation |
0.18* |
Sig. (bilateral) |
0.02 |
|
N |
151 |
|
Potatoes (unit) weekly consumption |
Correlación de Pearson |
0.32** |
Sig. (bilateral) |
0.001 |
|
N |
112 |
|
Green plantain (unit) weekly consumption |
Correlación de Pearson |
0.29** |
Sig. (bilateral) |
0.001 |
|
N |
116 |
|
Cabbage (unit) weekly consumption |
Correlación de Pearson |
0.45** |
Sig. (bilateral) |
0.000 |
|
N |
69 |
|
**. The correlation is significant at the 0.01 level (bilateral). |
||
*. The correlation is significant at the 0.05 level (bilateral). |
Table 9. Pearson Correlation Matrix between production and consumption. |
|||
Average amount consumed |
Total yearly production |
Red been (q) |
Black been (q) |
Been (lb) weekly consumption |
Pearson Correlation |
.379* |
.709 |
Sig. (bilateral) |
.043 |
.291 |
|
N |
29 |
4 |
Cause-effect relationships between the main variables of production, income, and consumption, in the context of the SSAN that occurs in the communities under study.
It was observed that in the monthly income of families for each community, there is a group of communities with higher incomes than others (not greater than C$ 17,562), such as the group of communities of Carlos Fonseca and Camilo Ortega, standing out for their high family income; the second group corresponds to the communities of Panama, Rigoberto López Pérez and Roa Padilla; as well as the third group corresponds in the Triumph of Sandino, Palo Solo, Las Gradas, Temoá, San Ignacio, Los Moncadas, Sandino and Pikín Guerrero. The following group of communities is among those with the lowest income compared to the aforementioned: German Pomares and Santiago. In the community July 19 and Cruz de Mayo, and finally the community that has the lowest income with respect to the previous ones is Loma Negra. In other words, 13 communities have very low income (not less than C$ 3,212) based on the other communities (Table 10 and 11).
Table 10. Analysis of the Variance for the variable Income |
|||||
F.V. |
SC |
gl |
CM |
F |
p-value |
Model. |
6.31 |
17 |
0.37 |
3.44 |
<0.0001 |
Community |
6.31 |
17 |
0.37 |
3.44 |
<0.0001** |
Error |
15.34 |
142 |
0.11 |
||
Total |
21.65 |
159 |
|||
The income variable was transformed Log10 |
Table 11. Test LSD de Fisher Alfa=0.05 DMS=0.31002 Error: 0.1080 gl: 142 |
|||
Community |
Mean |
n |
E.E. |
Loma Negra |
1737.8 |
8 |
0.12 A |
Cruz de Mayo |
2570.3 |
8 |
0.12 A B |
19 de Julio |
2951.2 |
10 |
0.10 A B |
Santiago |
3162.3 |
11 |
0.10 A B C |
German Pomares |
3235.9 |
10 |
0.10 A B C |
Pikín Guerrero |
3801.9 |
10 |
0.10 B C |
Sandino |
3890.5 |
8 |
0.12 B C |
Los Moncadas |
4073.8 |
10 |
0.10 B C |
San Ignacio |
4168.7 |
8 |
0.12 B C |
Temoá |
4168.7 |
8 |
0.12 B C |
Las Gradas |
4365.2 |
8 |
0.12 B C |
Palo Solo |
4466.8 |
10 |
0.10 B C |
Triunfo de Sandino |
4677.4 |
9 |
0.11 B C |
Roa Padilla |
6165.9 |
8 |
0.12 C D |
Rigoberto López Pérez |
6309.6 |
8 |
0.12 C D |
Panamá |
6309.6 |
8 |
0.12 C D |
Camilo Ortega |
11220.2 |
9 |
0.11 D |
Carlos Fonseca |
12022.6 |
9 |
0.11 D |
Mean with a common letter are not significatively different (p > 0.05) The value of mean is the monthly home income in Córdobas (Antilog base 10, real average) |
Citrus production in this area is presumed to be high, according to the data obtained, the ANOVA test carried out in the communities (Carlos Fonseca, Pikín Guerrero, Rigoberto López Pérez, Santiago and Triunfo de Sandino), was demonstrated with p = 0.8372 , that the communities produce citrus fruits equally (sweet orange, sour orange, sweet lemon and acid), with a general average of 3400, 6250, 1090, 2595 and 6147, respectively. This confirms the data of the National National Human Development Plan (PNDH), where citrus is ranked fourth as the item that occurs most in La Concepción. On the other hand, the National Agricultural Census (CENAGRO, 2001), shows that La Concepción contributes 50% of citrus production in the department of Masaya (Table 12).
Table 12. Analysis of the Variance for the “Annual citrus production” |
|||||
F.V. |
SC |
gl |
CM |
F |
p- value |
Model. |
0.78 |
4 |
0.20 |
0.35 |
0.8372 |
Community |
0.78 |
4 |
0.20 |
0.35 |
0.8372ns |
Error |
8.29 |
15 |
0.55 |
||
Total |
9.07 |
19 |
|||
The variable yearly citrus production was transformed Log10 |
In this research two types of consumption were defined for the communities under study, these are carbohydrates (rice, beans, tortillas, pinolillo, pasta, bag bread, potatoes, and green plantain) and proteins (beef, pork, poultry, fish, milk, eggs and dried cheese). It was possible to demonstrate how consumption behaves concerning communities, using the multivariate technique of Multivariate Analysis of Variance (MANOVA). The results obtained from MANOVA show that there are significant differences (p = 0.022) in carbohydrate consumption in rural communities (Table 14), while in proteins there is no significant difference (p = 0.209) in consumption in communities in study, that is, the families of different communities consume the same proportion of proteins per month (Table 13 and 14).
Table 13. Multivariate testsa (Carbohydrate) |
||||||
Effect |
Value |
F |
Gl hypothesis |
gl error |
Sig. |
|
Community |
Wilks Lambda |
.605 |
1.627b |
34.000 |
194.000 |
.022 |
Table 14. Multivariate testsa (Protein) |
||||||
Effect |
Value |
F |
Gl hypothesis |
gl error |
Sig. |
|
Community |
Wilks Lambda |
.625 |
1.292b |
16.000 |
78.000 |
.224 |
Conclusions
1. Among the main socio-economic characteristics that families presented in the communities, the following stand out: the male sex prevailed in the heads of households, with men mostly responsible for making decisions at home and mainly responsible for carrying resources to their families. The progress of the rural communities is potentially affected by the low level of schooling of the heads of families, of whom 36.90% have a primary education and even more alarming is the level of illiteracy that exists, which reaches 18%. Most of them have active employment, although with low income, with respect to the cost of the basic basket.
2. In general, it was established that with the basic food (rice, beans, oil, sugar, onion, green plantain, eggs), the rural family is supplied. The main associations between the variables of production, income, and consumption, in the communities under study, refer to the consumption of basic grains and the number of people in the household. A significant correlation between the number of people in the household and rice consumption was demonstrated, with r=0.381 (p < 0.05).
3. In particular, a significant correlation between the production and consumption of red beans was highlighted, with a r=0.379*. This evidenced that, in the families that are dedicated to the production of basic grains, only those families that produce red beans, use this item for their own consumption, that is to say, that as the main food product, the bean is prioritized in the consumption of the families producers. In relation to income and consumption, the income per month of the head of household is low, being below the cost per consumption of the basic basket of food products.
4. Regarding the cause-effect relationships between the main production and income variables: it was determined through ANOVA that the average income of rural families in each community has significant differences from each other, this shows that rural families have different monthly income, depending on the community in question. According to the criteria of the LSD test, it was shown that the communities with the highest income are: first, the Carlos Fonseca con and Camilo Ortega communities; secondly, the Panama, Rigoberto López Pérez and Roa Padilla communities; and lower-income communities: the Loma Negra community, followed by the Cruz de Mayo communities and the July 19 community. On the other hand, the municipality of La Concepción stands out for its production of citrus fruits (sweet orange, sour orange, lemon and sweet lemon). For the different communities under study, the ANOVA carried out on the citrus production variable determined that there are no significant differences between them.
5. Regarding the multivariate type relationships for the main consumption variables, two patterns of food consumption were determined. First: Carbohydrate. Second: Proteins. For the carbohydrate consumption pattern, the consumption variables were used: rice, beans, tortillas, pinolillo, pasta, bread, potatoes and green plantain, for which the most representative were obtained before the population under study, using the technique ACP multivariate, which allowed to define the CP1, with the most representative variables, which were rice, beans, pasta, and green plantain. The MANOVA carried out for the most representative variables of the carbohydrate group, determined the statistical evidence that consumption among rural communities has significant differences from each other. For the Protein consumption pattern, the consumption variables were used: beef, pork, poultry, fish meat, milk, eggs, and dry cheese, for which the most representative were obtained before the population in study, through the multivariate technique of the ACP, which allowed to define the CP1, with the variables of greater representation, which were beef, pork, poultry, and fish meat. The MANOVA performed for the most representative variables of the protein group, determined the statistical evidence that consumption between rural communities does not have significant differences between them.
Gratitude
Special thanks to the Vice Rectorate for Research of UNAN-Managua, where I was benefited by the Fund for Research Projects (FPI), which covered all the expenses of this research and thus it was possible to complete my thesis. We also thank the postgraduate research department which was the bridge to participate in this opportunity to qualify and thus obtain funding.
I thank God for having accompanied and guided me throughout my major, for being my strength in moments of weakness and giving me a life full of learning. I thank my parents Ulises and Georgina for supporting me at all times, for the values they have instilled in me, and for allowing me to have an excellent education in the course of my life. To Deborah Murillo, for being a very important part of my life, for having supported me in good times and bad times.
I appreciate the trust, support, and dedication of time to my teachers: PhD. Manuel Enrique Pedroza, Dr. Sebastián Gutiérrez, MSc. Ricardo Orozco, MSc. David Garcia For having shared with me their knowledge and especially their friendship.
Thanks to the Mayor’s Office of La Concepción for believing in me and for providing the opportunity to develop my professional thesis for all the support and facilities that were granted. For allowing growing professionally and learning new things. To my third year colleagues in the major of Statistical Engineer who was very supportive in my thesis and the field.
Works Cited
Bizquerra Alsina, R. (1989). Introducción conceptual al Análisis multivariables. Edit. PPU. Barcelona. P 5, 29 – 31, 178, 260 y 295 – 296.
Canales, Alvarado y Pineda (1994). Metodología de la Investigación, Manual para el
Casanoves, F. (2007). Técnicas de Análisis Multivariado. Curso Internacional. CATIE.
Discovskiy L. (2002). Folletos del curso “Estadística Aplicada para Análisis de Encuesta en SPSS para Windows” ADESO. Estelí, Nicaragua.
FAO. (2013). La experiencia del programa España-FAO para America Latina y el Caribe. [Version electronica]. Cooperacion Internacional y Politicas Publicas de Seguridad Alimentaria y Nutricional. Retrieved on September 1, 2015 from http://www.fao.org/documents/card/es/c/6e16c8ec-c96c-540e-917e-44f0431b8883/
Garcia (2015). Presentacion del Analisis de Componentes Principales. UNAN-Managua.
Hernández, R., Fernández, C. y Baptista, P. (2006). Metodología de la Investigación. 4ta Ed. México: McGraw-Hill Interamericana. 863 p.
Hambre Cero. (2011). Tercer Informe de Evaluacion del Programa Productivo Agropecuario. Hambre Cero: Avences y Desafíos. Retrieved on September 1, 2015 from www.ieepp.org/wp-content/plugins/download.../download.php?id=148
INIDE (2005). Mapas de pobreza extrema municipal por el método de necesidades básicas insatisfechas (NBI) [Versión electrónica]. Retrieved on September 1, 2015 from http://www.inide.gob.ni/censos2005/cifrasmun/mappobrezad.pdf
INIDE. (2014). Instituto Nacional de Información de Desarrollo. [Version electronica]. Obtained from de Instituto Nacional de Información de Desarrollo: http://www.inide.gob.ni/
Marín, C.R. (2014). Fortalecimiento institucional y gestion integral del riesgo a desastres con enfoque alimentaria y nutricional, en los doce municipios miembros de la asociacion de municipos de Nueva Segovia. Tesis de maestria regional en seguridad alimentaria y nutricional con enfasis en gestion local. UNAN-Managua. Managua. Nicaragua. 180 p.
Montgomery, D.C. (1991). Experimento con un solo factor y mas sobre experimentos unifactoriaes. Diseño y Analisis de Experimentos. (pp. 45-116). Iberoamericana.
Perez (2009). Tecnicas Estadisticas Multivariantes con SPSS. Analisis multivariable de la varianza y de la covarianza. (pp 217 - 223). Garceta.
Piura, J. (2006). Metodología de la Investigación Científica, Un enfoque integrador. Primera Ed. PAVSA. Managua, Nicaragua. 254 p.
Pedroza, M.E. y Dicovskiy, L. (2006). Sistema de Análisis Estadístico, con SPSS. Primera Edición. Managua INTA / IICA. 139 p.
Walpole, Ronald E; Raymond H. Myers; Sharon L. Myers y Keying Ye. (2007). Probabilidad y estadística para ingeniería y ciencias. Estadística no paramétrica (octava edición) (pág. 684). Pearson: México.