Keywords:

Typication

Family farming

Vegetables

Spring onion (Allium stulosum L.) farmer’s system typologies of Maracaibo municipality,

Zulia State, Venezuela

Tipología de productores de cebollín (Allium stulosum L.) del municipio Maracaibo, Estado Zulia,

Venezuela

Tipologias de produtores de cebolinha (Allium stulosum L.) no município de Maracaibo, Estado de

Zulia, Venezuela

Arlenis Albornoz*

Fátima Urdaneta

Rev. Fac. Agron. (LUZ). 2025, 42(4): e254247

ISSN 2477-9407

DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v42.n4.IV

Socioeconomics

Associate editor: Dr. Jorge Vilchez-Perozo

University of Zulia, Faculty of Agronomy

Bolivarian Republic of Venezuela

Dpto. de Ciencias Sociales, Facultad de Agronomía.

Universidad del Zulia. Venezuela.

Received: 22-07-2025

Accepted: 08-09-2025

Published: 08-10-2025

Abstract

In Venezuela, spring onions are the most widely consumed edible

leafy vegetable. Their production is concentrated in small settle-

ments where the interaction of farmers with social, technical, eco-

nomic, environmental, and territorial factors gives rise to a dierent

types of production systems. The study aimed to typify the spring

onion production systems in the municipality of Maracaibo, Zulia

state, Venezuela. A sample of 53 farmers was considered, to whom

was applied a structured questionnaire with sociodemographic, la-

bor, technology, territory, natural environment, and socioeconomic

enviroment information. The groups were formed using multivaria-

te techniques (Principal Components and K-Means Clustering) and

were compared using Chi-square. The four groups were: 1. Mixed

Family Production Systems (MFS = 36 % of the sample), centered

on family labor, that combined agricultural crops with small-sca-

le animal husbandry and the use of organic fertilizer. 2. Intensive

technology systems (ITS = 23 %), where chemical fertilizers (ni-

trogen and phosphorus) were used intensively. 3. Family polycul-

ture systems (SPF = 28 %) cultivated spring onion and other crops

(cilantro, cassava, and plantain) for commercial sales. 4. Technied

polyculture systems (SPT = 13 %), which were labor-intensive,

planted large areas of spring onions and other crops (plantain and

“topocho”), performed a more ecient use of inputs, and pests con-

trol. These ndings help to understand specicities of each typolo-

gy, that allow personalized implementation of agricultural develop-

ment strategies, addressing specic factors for each group.

This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.

Rev. Fac. Agron. (LUZ). 2025, 42(4): e25424 October-December. ISSN 2477-9409.

2-7 |

Resumen

En Venezuela, el cebollín representa la hortaliza de hoja comestible

de mayor consumo, su producción se concentra en pequeños asen-

tamientos en los cuales la interacción del agricultor con factores

sociales, técnicos, económicos, ambientales y territoriales, originan

diferentes tipologías de sistemas de producción. El estudio tuvo como

objetivo tipicar los sistemas de producción de cebollín del munici-

pio Maracaibo, estado Zulia, Venezuela; se consideró una muestra de

53 agricultores a quienes se les aplicó un cuestionario estructurado

con información sociodemográca, fuerza laboral, tecnología, terri-

torio, ambiente y entorno. Los grupos se conformaron por medio de

técnicas multivariadas (Componentes Principales y Conglomerados

por K-medias) y se compararon con Chi cuadrado. Los cuatro grupos

fueron: 1.- Sistemas de Producción Mixta Familiar (SMF = 36 % de

la muestra), centrada en la mano de obra familiar, combina cultivos

agrícolas con cría de animales a pequeña escala y uso de fertilización

orgánica. 2.- Sistemas de tecnología intensiva (STI = 23 %), donde

se utilizan intensamente fertilizantes químicos (nitrogenados y fos-

forados). 3.- Sistemas de policultivos familiares (SPF= 28 %) culti-

van cebollín y otros cultivos (cilantro, yuca y plátano) para la venta

comercial. 4.- Sistemas de policultivos tecnicados (SPT = 13 %),

con uso intensivo de mano de obra,

siembran grandes extensiones de

cebollín y de otros cultivos (plátano y topocho), utilizan más ecien-

temente los insumos y contrarrestan plagas. Estos hallazgos ayudan

a comprender las particularidades de cada tipología permitiendo la

implementación personalizada de estrategias de desarrollo agrícola,

interviniendo en factores precisos para cada grupo.

Palabras claves: tipicación, agricultura familiar, hortalizas.

Resumo

Na Venezuela, a cebolinha é o vegetal folhoso comestível mais consu-

mido. Sua produção concentra-se em pequenos assentamentos onde

a interação dos agricultores com fatores sociais, técnicos, econômi-

cos, ambientais e territoriais dá origem a diferentes tipos de sistemas

de produção. O estudo teve como objetivo tipicar os sistemas de

produção de cebolinha no município de Maracaibo, estado de Zulia,

Venezuela. Foi considerada uma amostra de 53 agricultores, aos quais

foi aplicado um questionário estruturado com informações sociode-

mográcas, força de trabalho, tecnologia, território, meio ambiente

e entorno. Os grupos foram formados usando técnicas multivariadas

(Componentes Principais e Agrupamento K-Means) e comparados

usando Qui-quadrado. Os quatro grupos foram: 1. Sistemas de Pro-

dução Familiar Mistos (MFS = 36 % da amostra), centrados na mão

de obra familiar, combinando culturas agrícolas com criação de ani-

mais em pequena escala e uso de fertilizantes orgânicos. 2. Sistemas

de tecnologia intensiva (ITS = 23 %), onde fertilizantes químicos

(nitrogênio e fósforo) são usados intensivamente. 3. Sistemas de po-

licultura familiar (SPF = 28 %) cultivam cebolinha e outras culturas

(coentro, mandioca e banana-da-terra) para venda comercial. 4. Sis-

temas de policultura tecnologicamente avançados (SPT = 13 %), que

exigem muita mão de obra, plantam grandes áreas de cebolinha e ou-

tras culturas (banana-da-terra e topocho), fazem uso mais eciente de

insumos e combatem pragas. Essas descobertas ajudam a compreen-

der as especicidades de cada tipologia, permitindo a implementação

personalizada de estratégias de desenvolvimento agrícola, abordando

fatores específicos para cada grupo.

Palavras-chave: tipificação, agricultura familiar, hortaliças.

Figure 1. Study area reference map for the spring onion (Allium

stulosum L.) production systems typology.

Introduction

The production of spring onions (Allium stulosum L.) in the

state of Zulia, Venezuela, is mainly carried out by family production

systems; in which small-scale agriculture is practiced. This crop is

alternated with other agricultural and animal products (Albornoz

and Maldonado, 2022) where social, technological, economic,

environmental, and territorial factors are combined and interrelated.

In spite of the fact that most producers want to and try to improve their

production levels, not all of them have the capacity to do so, because

such capacity depends mainly on the way factors and resources are

combined.

This gives rise to dierent types or modes of

production that reveal

the interactions that occur in the production units, considering factors

related to both the natural environment and the

territory, which can

generate well-being for farmers and their families (Albornoz, 2024)

as reected in the economic and productive results of each type of

production system, the identication and characterization of these

types is essential for the implementation of eective and sustainable

actions (Martínez et al., 2021). Furthermore, to

the agricultural

research community and institutions, it is an essential step for the

success of extension and technology transfer programs for farmers

(Stringer et al., 2020).

The understanding of the structure of dierent systems types

ensures the eectiveness of agri-sector development actions

(Ouédraogo and Tapsoba, 2022), besides classication allows

to analyse agroecosystems diversity, agricultural producers

characteristics, their production systems and the possible relationships

that may arise (Goswami et al., 2014).

Hence, the importance of creating more or less homogeneous

groups based on similar characteristics (Álvarez et al., 2018). In

this context, the study aimed to typify the agricultural systems in the

main onion spring production region of Zulia state and thereby obtain

information that allows the implementation of a more specialized

approach to design agricultural development strategies (Tirado et al.,

2021), which eectively intervene in specic factors in each tipology

and simplify options selection for planning strategies or processes

implementation related to the production system competitiveness and

sustainability of the territory (Zuluaga et al., 2023).

Materials and methods

The study was conducted in the Maracaibo municipality of

Zulia state, Venezuela, located in the far northwest of the country,

specically in four parishes: San Isidro, Francisco Eugenio

Bustamante, Venancio Pulgar, and Antonio Borjas Romero (Figure

1); between the coordinates 10°43’06.5”N 71°45’53.3”W and

10°35’53.8”N 71°43’41.9”W; territories where agricultural activity

plays a signicant role in the local and regional economy.

N*p*q*Z

(N-1)+p*q* Z

This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.

Albornoz & Urdaneta. Rev. Fac. Agron. (LUZ). 2025, 42(4): e254247

3-7 |

K-means cluster analysis was performed using the new factors

extracted from the PCA; Four clusters were obtained using the non-

hierarchical algorithm (K-means), since this number showed a solid

classication by optimizing the distribution of the systems among the

groups and by minimizing each observation sum of distances with

respect to the center of its group. The characteristics of each typology

resulting from the structure of each one were expressed in frequencies;

the Chi-square test (X

) was also used for group comparisons. IBM

SPSS Statistics version 23.0 was used for all statistical analyses.

Results and discussion

General characteristics of producers and production systems

Spring onion (chive) production in Zulia state was performed in

small production units which size ranged from 1 to 5 hectares, 66

% of the analyzed sample showed a total area of 2 hectares or less.

These production units constitute the household of the farmer and

his family (80 %), families were made up of 5 ± 2.38 members on

average; results that were consistent with reports from horticultural

systems in Chile, Peru, Colombia, and Ecuador (Boza et al., 2019;

Rocha et al., 2016; Tirado et al., 2021; Verdezoto & Viera , 2018) .

Male farmers (90 %) showed a mean age of 48 ± 12.68 years,

with a 50-year dierence between the youngest farmer (24) and the

oldest. Despite this dierence, 72 % of the farmers were under 55

years of age, they are still in working age and economically active.

These results are quite coincident with the ages of small farmers in

Chile, who ranged from 15 to 65 years (Vera & Moreira, 2009).

Regarding the educational level of producers, 51 % managed to

complete primary education and 43 % secondary education, wich

is reected in the empirical technologies they use, since there is

evidence that establishes a positive relationship between the level

of education and the adoption of new technologies (Bidogeza et al.,

2009), in addition, educated people perform jobs and functions more

eciently, important in decision-making at home. In general terms,

it can be said that they were small family production systems that

constitute the farmer’s household, with the man acting as head of the

family and as the laborer of the production unit.

Explanatory factors selection for structuring farmer’s system

typologies

Those variables whose explanatory power was greatest were

selected from the 38 variables considered in the study. Pearson’s

correlation analysis of the selected variables matrix yielded a

This research was framed under the empirical-inductive approach;

mainly descriptive type, with an ex post facto, non-experimental,

cross-sectional anf eld design; (Hernández et al., 1997; Padrón,

2007). To estimate the study population, Google Earth for Windows

(License: Freemium, version 10.75.03, March 2025) was used to

visualize, identify, and georeference agricultural production units

using satellite images showing the area planted with chives. Guided

tours were conducted with key stakeholders from each area. Thus,

145 production units were identied in the four parishes. The sample

population was calculated using the formula proposed by Martínez

(2005), which states:

Where:

N = Population size (145)

p = Probability of success (50 %)

q = Probability of failure (50 %)

Z = Standard distribution or 90 % condence level

e = Population mean error (9 %)

A sample of 53 agricultural

production units (PU) was dened, using

the stratied random sampling technique by proportional allocation,

which distributed the sample according to the relative weight (size) of

each stratum. The criteria for selecting the units were: 1) Production

units in which chives (spring onion) were the main production item;

2) units in which more than 40 % of the cultivated area was chives. To

collect the data, a questionnaire was applied to farmers wich was made

up by 30 items, distributed in six dimensions (Table 1).

The typologies of production systems were obtained after applying

a factorial extraction analysis by principal components (PCA), both

widely used in studies of agricultural production systems (Barnes and

Toma, 2012).

Several matrices were structured which began with the total of the

available variables, but those that showed a factorial weight below 0.5

were removed from the analysis. Bartlett's test of sphericity (Zimpel

et al., 2017), the determinant coecient, and the KMO test were

also used to select the nal matrix for the PCA analysis. To select the

number of components, the Kaiser criterion (Silva et al., 2020) was

used, and according to this, variables with eigenvalues greater than

1.0 were retained; the criterion of minimum cumulative variance of

60 % was also applied.

Table 1. Dimensions and variables considered to classify spring onion production systems.

Dimensión Variables

Sociodemographic data of the farmer and his family

Sex, age, education, producer experience, marital status, composition of the family nucleus, permanence

(lives in the PU), land ownership

Production unit and workforce data

Land size and uses, productive área or number of “muros”*. Number of agricultural items produced.

Type and quantity of labor

Local Tecnology

Soil preparation, seeding, irrigation, fertilization

Pest control, weed control, and harvesting

Territory

Strengths or Weaknesses

Quality of soils, quality of roads, proximity to the city, personal security

Natural environment

Water source.

Biodiversity.

Biotic resources.

Waste management .

Socioeconomic environment

Marketing.

Policies.

Agricultural support and agricultural services.

*Each “muro” measures on average 50 m long, 1,20 m wide and 0,30 m high

N*p*q*Z

(N-1)+p*q* Z

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Rev. Fac. Agron. (LUZ). 2025, 42(4): e254247 October-December. ISSN 2477-9409.

4-7 |

determinant coecient of 0.255, the Kaiser-Meyer-Olkin index

showed a value of 0.552 (> 0.5), and the Bartlett test of sphericity

reported a signicance of P = 0.000, indicating a sucient relationship

between the variables and suitability for conducting PCA.

The results of the analysis showed the selection of thirteen variables

(Table 2) with the greatest explanatory power that is indicated by

the communalities, which refer to the total amount of variance that

each variable retains in the factors and that can be explained by the

factorial model obtained. The highest values of the variables number

of produced items and biodiversity index indicate that the model

is able to reproduce 93.7 % and 93.5 % of the original variability

of each one, likewise, it can be observed that it also explains more

than 75 % of each of the rest of the variables; however, the lowest

value that corresponded to the variable fertilization frequency is still

considered acceptable to include in the analysis since it indicates that

the model is able to reproduce 56.3 %.

Likewise, the PCA results shown in Table 2 indicate that these

ve selected components or factors (CPs) showed eigenvalues greater

than 1 and retained 79.9 % of the total variance of the 53 spring

onion production systems. The variables that explain or describe

each component are shown in the rotated factor matrix (Varimax) of

independent variables with the weight factor for each variable.

The rst component (CP1) is most strongly correlated with the

planting of other crops, the number of items in production, agricultural

diversication, and income from other items or non-agricultural

activities; and less strongly correlated with the proportion of labor

and the location of the UP far from the city. The second component

(CP2) is strongly correlated with the importance of living close to the

city and intensive fertilization technology, but negatively correlated

with the lack of quality land.

The third component (CP3) it is described by the security that

territory provides contrasting with land routes in poor condition; the

fourth component (CP4) is strongly related to larger areas (number

of “muros”) planted with spring onions and a to a high proportion

of labor; and nally, the fth component (CP5) represents chive

production experience associated with animal husbandry. These

components indicate the existence of groups of production units with

specic characteristics.

Farmers typologies formation

Four clusters were identied (Figure 2), each one corresponding to

a type of farmer; cluster 1 represents 36 % of the production systems;

cluster 2 is made up by 23 % of the systems analyzed; cluster 3 is

formed by 28 % and the fourth represents 13 % of all.

It could be noticed that cluster one represents production systems

whose typology is mostly described by CP3 and CP5 since the

security provided by the territory is very important to these farmers;

also, their chives growing experience stands out, which they have

associated with animal prodution. To this group of farmers, the size

of the planting area, the number of labor and the technology are not

very relevant, however, they do carry out organic fertilization, so they

can be classied as producers with mixed family systems (MFS).”

Regarding cluster 2, it was observed that CP2 which is referred to

the city near location most describes this group, these farmers attach

great importance to the fact that the production units are near to the

cities, and also to a intensive fertilization, hence they were classied

as producers with intensive technology spring onion production

systems (ITS).”

On the other hand, cluster 3 is mainly related to the variables that

described CP1, which are associated to production systems where

farmers plant other crops in addition to spring onion, they gave great

importance to the number of agricultural production items, thus

diversifying agriculture and income with other production items and

also with non-agricultural activities, therefore, they were classied as

producers with family polyculture chive production systems (SPF).

Finally, cluster four is most closely related to CP4 in terms of the

size of planted área or number of “muros” and to the number of hired

labor; it is also related to a lesser degree to CP1 and CP2, so it is a

group that represents production systems with larger planted areas

and with high use of labor, high fertilization, high biodiversity, crop

association and non-agricultural income. These factors allow them to

be classied as producers with thechnied polyculture spring onion

production systems (TPS).

Table 2. Rotated component matrix results of principal components analysis with the evaluated variables.

Rotated component matrix

Variables

CP1 CP2 CP3 CP4 CP5

Comunalidades

EXPERIENCE -0,129 0,291 -0,207 0,139 0,783 0,776

OTHERINCOM 0,740 -0,083 0,188 -0,077 0,400 0,756

OTHERCROP 0,857 0,101 -0,022 0,041 -0,295 0,834

ANIMPRODUC 0,403 -0,205 0,108 -0,441 0,612 0,785

NUMPRODUTC 0,939 0,104 -0,200 0,058 0,037 0,937

NUMBMUROS 0,079 -0,090 0,131 0,871 -0,001 0,790

FERTFREC 0,105 0,599 0,370 -0,066 -0,228 0,563

WORKFORCE 0,027 -0,003 0,016 0,921 0,011 0,849

GOODSOIL -0,148 -0,865 0,097 -0,026 -0,146 0,801

INSECURITY -0,108 -0,031 0,872 0,130 -0,044 0,792

NEARCITY -0,024 0,865 -0,118 -0,059 0,111 0,779

QUALIROADS 0,108 0,030 -0,883 -0,018 0,042 0,794

BIODINDEX 0,937 0,104 -0,201 0,060 0,040 0,935

EXPERIENCE: farmer years experience; OTHERINCOM: another income source; OTHERCROP: other produced crops; ANIMPRODUC: animal production; NUMPRODUTC: number of

produced items; NUMBMUROS: chives production number of “muros” ; FRECFERT: fertilization frecuency; WORKFORCE: number of labor; GOODSOIL: It is a strength to have good soil;

INSECURITY: Insecurity is a weakness; NEARCITY: Located near to the city is a strength; QUALIROADS: The bad quality of the roads is a weakness. BIODINDEX: biodiversity index. (Source:

Output from IBM SPSS version 23.0 software with own data)

This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.

Albornoz & Urdaneta. Rev. Fac. Agron. (LUZ). 2025, 42(4): e254247

5-7 |

Figure 2. Clusters graphical representation according to the

weight of each component within group.

Dierentiating factors of the spring onion farmer’s system

typologies

The Chi-square analysis showed that variables: animal production

(p≤ 0.00), number of “muros” (p≤ 0.00), fertilization frequency (p≤

0.00), labor (p≤ 0.00), security in the territory (p≤ 0.00), proximity to

the city (p≤ 0.02) and road conditions (p≤ 0.00), were associated to

the typologies; in other words, each of these variables had a dierent

behavior in each typology, the values of the variable categories

allowed to characterized the groups particularities (Table 3).

Farmers with mixed family systems (MFS)

These producers focus on combining agricultural activities of

crops and small-scale animal husbandry, mainly sheep and poultry,

which they produce for sale, self-consumption or for the exchange of

goods whith others farmers in the area, thus they compensate family

income. In this type of group, labor was limited to one or two people

(89 %), represented primarily by the farmer and his or her direct

descendants, whether son or daughter, in this regard, Kuivanen et

al. (2016) consider that they are the traditional backbone of the rural

workforce, carrying out the dierent tasks of the crop (Guillen, 2020),

which may be related to the number of production chive “muros”; 47

% of the MFS farmers cultivate between 36 and 70 chive “muros”,

equivalent to an area between 0.5 and 1 hectare of production. These

farmers and their families have more than 15 years of experience

(42 %) in agricultural activity. The main objective of the farmer is to

provide family food by producing self-consumption items.

These producers consider that geographic location close to the

city denotes a strength for the system, it was expressed by 67 % of

farmers; in addition, 74 % of these producers also consider a strength

the good quality soil available for chives production. Finally, this

group consider that personal insecurity is not a problem in their

territory, thanks to the community eorts that leaders in the area carry

out to maintain safety of all.

Farmers with intensive technology systems (ITS)

The production systems of this group represent 23 % of the

sample, and their most signicant characteristic is the intensive use

of chemical fertilizers. 83 % of farmers in this group apply these

fertilizers three times per crop cycle, a frequency that diers from the

rest of the groups and suggests that this is a fundamental production

practice. According to Bouteska et al. (2024), this activity plays a

crucial role in increasing crop yield.

This typology is more oriented toward vegetable production; 67

% of these farmers grew other crops such as plantains, “topocho”

(another musaceae), and cassava in addition to spring onion, which

require fertilizers. Another reason for fertilizers use is that 75 % of

farmers think that soil is not good enought for growing spring onion.

This group includes producers with less experience growing chives;

42 % have less than ve years of experience, representing the new

generation that makes agriculture their way of life. Regarding the

territory, they considered that near location to the city is benecial

(67 %), especially because they have more opportunities to access

agricultural and household inputs, even though the conditions of

agricultural roads are a factor that hinders the system (75 %). Finally,

this cluster showed a medium to low biodiversity index, that is a

consequence of the limited diversication they performed, since most

of the land is dedicated to chive cultivation.

Farmers with family polyculture systems (FPS)

This third group represents 28 % of the sample, farmers produced

other crops commercially in 73 % of the production units (PUs),

unlike the MFS type, where production was for self-consumption. In

this case, 80 % of producers grew three or more crops in addition to

spring onion, generally associated with cilantro, cassava, “topocho”,

and plantain, as the most representative crops. Consequently, this

group exhibited medium to high agricultural biodiversity (47 % and

33 % respectively) and the smallest areas planted with chives (fewer

than 35 “muros”). Thus, farmers in this group obtained additional

income from other crops sale (53 %).

Regarding agricultural experience, it could be seen that farmers

have been growing spring onion for more than 15 years (47 %). In

these production systems, labor was minimal due to the small planted

areas for each crop, that is why in 67 % of PUs labor is limited to one

person that played de role of the farmer and the head of the household.

This is an indicative that other family members nd themselves in

need of other income sources to meet household requirements. When

considering farmers’ appreciation of soil quality, opinions are divided

and unlike the others, it is striking that this group considers insecurity

to be an element that negatively aects the productive system.

Farmers with technied polyculture systems (TPS)

This last group represents producers with primarily plant-

based agricultural production systems and accounts for 13 % of the

sample. They rely on larger-scale crop combinations, by optimizing

pollination, nutrient uptake, and pest control. This group was

characterized by their intensive use of labor; the producers in this

group were the only ones who hired personnel in order to attend

seasonal activities, primarily planting and harvesting. 43 % employ

three people, while 57 % employ four or more.

In these systems there were large extensions of spring onion

cultivated land since 83 % of farmers mantained more than 100 “muros”

in production, equivalent to more than 2 hectares, in addition 71 % of

them grow other crops, both commercially and for self-consumption.

The main crop combinations were chives - plantain - cassava (43 %);

chives - cilantro - plantain and cassava (43 %), while the chives - plantain

combination was in lesser proportion (20 %), by considering previously

exposed, biodiversity index for this group ranged from médium to high

values; this diversication is an opportunity to generate alternative

income, contribute to the economy improvement and mitigate critical

situations that sometimes arise in this productive activities, in turn,

agricultural biodiversity reinforces food security (Aquino et al., 2018)

This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.

Rev. Fac. Agron. (LUZ). 2025, 42(4): e254247 October-December. ISSN 2477-9409.

6-7 |

Table 3. Frequency distribution of the study variables by farmer’s system typologies.

Indicator

Categories Farmer’s system typologies

1 MFS

n=19

2 ITS

n=12

3 FPS

n=15

4 TPS

n=7

Farmer years experience

≤ 5 años 21 %

42 %

27 % 29 %

( 6-10 años)

26 %

17 % 13 % 14 %

(11-15 años) 11 % 17 % 13 % 14 %

> 15 años 42 % 25 %

47 %

43 %

Another income source

Others crops

68 %

25 % 53 %

71 %

Subsidies and/or non-agricultural activities 26 %

33 % 27 %

14 %

None 5 % 42 % 20 % 14 %

Other produced crops

yes 58 % 67 %

73 % 86 %

No 42 % 33 % 27 % 14 %

**Animal production

yes

74 %

8 % 47 % 0 %

No 26 % 92 % 53 % 100 %

Number of produced items

Four or more 21 % 0 % 33 %

43 %

Three 42 %

58 %

47 % 43 %

Two

37 %

42 % 20 % 14 %

** Spring onion production

(number of “muros”)

≤ 35 11 % 33 %

40 %

0 %

36 a 70

47 %

42 % 27 % 0 %

71 a 100

21 %

17 % 20 % 14 %

> de 100 21 % 8 % 13 %

86 %

**Fertilization frecuency

One 21 % 0 %

53 %

14 %

Two 47 % 17 % 33 %

57 %

Three 32 %

83 %

13 % 29 %

** Number of labor

One

63 %

50 % 67 % 0 %

Two 26 %

50 %

27 % 0 %

Three 11 % 0 % 7 %

43 %

Four or more 0 % 0 % 0 % 57 %

It is a strength to have good soil Si

74 %

25 % 60 % 29 %

** Insecurity is a weakness Si 0 % 8 %

80 %

14 %

** Being close to the city is a strength Si 5 %

67 %

27 % 43 %

The quality of the roads is a weakness Si

89 %

75 %

0 %

57 %

Biodiversity index

0,75 (Alta) 21 % 0 % 33 %

43 %

0,50 ( Media) 42 %

58 %

47 % 43 %

0,25 (Baja) 37 %

42 %

20 % 14 %

MFS: Farmers with mixed family systems, ITS: intensive technology systems, FPS: farmers with family polyculture systems, TPS: farmers with technied polyculture systems. ** Signicativo a

p≤ 0,01. Source: Own data. Output from SPSS software version 23.0

Farmers of this type showed more than 15 years of experience (43

%), who consider the soil to be of poor quality (71 %). Due to these

systems have the largest land areas, the focus is on safety; 86 % of

farmers do not have such problems, as crops are generally monitored

at night during the harvest season.

Conclusions

Sprin onion farmers from Maracaibo municipality, Zulia state,

were classied into four types of production systems, The distinctive

factors were animal husbandry, planting area, fertilization frequency,

labor type and number, and the strengths and weaknesses of the

territory. In general terms, the predominance of family labor indicates

that these are characteristic family farming systems.

In this regard, it could be arm that two of the typologies were

characterized by an exclusive role of the farmer and his family to

agricultural tasks, one of them were dedicated to produce spring

onion and also animal husbandry (Mixed Family System) and the

other combine spring onion production with other crops (Family

Polyculture System).

The other two typologies were distiguished by technology and

labor, in one hand was the intensive application of technological

This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.

Albornoz & Urdaneta. Rev. Fac. Agron. (LUZ). 2025, 42(4): e254247

7-7 |

resources, such as chemical fertilizer (Intensive Technology System)

and in the other hand was the intensive use of hired labor in order to

meet high level production and crops diversity (Technied Polyculture

System).

Regarding the territory, insecurity is a factor of concern to those

groups dedicated to polyculture (FPS and TPS), since intercropping is

attractive to those near lawbreakers, given the peri-urban location of

these systems.

These ndings help to understand the particularities of each

typology by allowing the personalized implementation of agricultural

development strategies, which intervene in precise factors for

each group. They can also promote training programs to improve

agricultural practices, when recommending biological diversity as

a strategy to generate alternative income, improve environmental

sustainability and enhance food security by addressing the weaker

qualities within each typology.

Finally, it is important to encourage participatory action

research among farmers as an alternative for supporting studies

with experimental trials that address key problems and contribute to

generate research for academies and institutions that are nancially

limited to conduct studies.

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