The Influencing Factors of Farming Households’ Production

Decisions

Xiaonan Fan

, Ye Wang

and Minghua Dai

2,*

School of Management, Dalian Polytechnic University, Paoai Street, Dalian, China

School of Management, Dalian Polytechnic University, Dalian, China

Keywords: Production Decisions, Risk Attitudes, Logistic Regression Models, Economic Management.

Abstract: The rural village of Huanong in the hilly region of Liaodong is the This article is based on the research site,

where most of the residents are engaged in agriculture due to historical and cultural influences, and therefore

agricultural income has become their main economic source, but the villagers are unable to make decisions

that are conducive to increasing their income due to various factors. Therefore, it is an urgent problem to find

the factors that influence the production decision of farmers. Based on this, this paper mainly uses a logistic

binary logistic regression model to conduct descriptive statistics and empirical analysis on the factors

influencing farmers' production decisions. Using big data and information technology to analyze the

influencing factors, it is found that the years of education of the household head has a significant influence on

the production decision of farmers, the income of farmers and the area of farmland owned by farmers have a

positive influence on the production decision of farmers, and the risk-averse attitude of farmers hinders the

rational decision of farmers. Finally, scientific and effective suggestions are provided to farmers based on

these factors affecting farmers' production decisions.

1 INTRODUCTION

With the continuous development of information

technology of big data, the digital technology

represented by Internet, cloud computing, big data,

Internet of Things and artificial intelligence is

becoming more and more prominent in the world

economy, and the deep integration of information

technology and traditional industries is releasing

powerful vitality. The Central Document No. 1,

"Opinions of the Central Committee of the

Communist Party of China and the State Council on

Deepening the Structural Reform on the Supply Side

of Agriculture and Accelerating the Cultivation of

New Dynamic Energy for Agricultural and Rural

Development" proposes to optimize the structure of

agricultural practitioners and improve the quality of

decision-making. Therefore, the use of big data and

information technology to improve farmers' overall

skills and management knowledge provides great

help for production decisions.

Since villagers in rural Hua have been making a

living by farming for generations, yet making little

profit from it, this makes them negative about the next

year's planting decisions and the cycle fails to achieve

true wealth. Therefore, it is important to investigate

the basic situation of rural Chinese farmers, their

production income and investment status, as well as

their response to market changes, to analyze the

relationship between farmers' increase in production

inputs and their basic situation and response to the

market, and to provide farmers with scientific

suggestions and opinions to help them make

reasonable production decisions and increase their

income based on the analysis of factors affecting

production decisions.

In order to have a comprehensive understanding

of how farmers make production decisions, this study

was based on a survey of 195 rural households in

China. A questionnaire was distributed to understand

the production status of farmers. The study was

conducted by combining the theories of economics,

statistics and management to investigate the

production decision making behavior of farmers.

Fan, X., Wang, Y. and Dai, M.

The Inﬂuencing Factors of Farming Households’ Production Decisions.

DOI: 10.5220/0012026200003620

In Proceedings of the 4th International Conference on Economic Management and Model Engineering (ICEMME 2022), pages 89-94

ISBN: 978-989-758-636-1

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

2 QUESTIONNAIRE

2.1 Questionnaire Design

This questionnaire contains the basic personal

information of the farmers, the production status of

the farmers, and the capital investment status of the

farmers. The first part of the questionnaire is to

understand the basic situation of the farmers, because

this survey is household-based, so the basic

information of the farmers refers to the basic situation

of the person who is responsible for making decisions

in the household, i.e., the head of the household. The

second part of the survey is about the production input

status and decision-making of farmers. The third part

investigates the risky behavior in the crop-growing

process, from which the influence of farmers' risky

attitudes on decision-making is understood. The

fourth section investigates the association between

market price changes and farmers' production

decisions. It provides accurate and sufficient data for

future analysis of factors influencing farmers'

production decisions.

2.2 Questionnaire Distribution

This questionnaire survey was conducted with a

sample of 195 households in rural China, all of which

were native villagers of the village and owned their

own land. The total number of questionnaires

distributed in this survey was 195, and the total

number of questionnaires collected was 180. Among

the 180 questionnaires collected, 5 invalid

questionnaires were excluded for the following

reason: the farmers in these five questionnaires chose

to work outside the village and rented out their land

use rights to collect rent as their income, which was

not relevant to the analysis of the factors influencing

the production decisions of the farmers in this study.

In summary, 195 questionnaires were distributed, of

which 175 valid questionnaires were recovered, and

the questionnaire recovery rate reached 89.74%. The

data were entered using EXCEL software to ensure

the completeness and accuracy of the data for later

analysis using SPSS software.

2.3 Reliability Test of the

Questionnaire

The reliability analysis of questionnaires is divided

into two ways: intrinsic reliability analysis and

extrinsic reliability analysis. Intrinsic reliability is

mainly used to examine whether a set of evaluation

items is set to measure the same concept and to

examine whether there is a relatively high degree of

internal consistency among the items. The Alpha

reliability coefficient method is currently more

commonly used in the statistical field. It is usually

specified as shown in Table 1. Tables 2 and 3 show

the reliability and validity results of the questionnaire.

Table 1: Criteria for judging the Kronbach alpha coefficient.

Coefficie

≥0.9 0.9>α≥0

0.8>α≥0.7 α<0.7

Meaning High

intrinsic

credibili

Intrinsic

reliabilit

y is

acceptab

Scale

design is

problemati

c, but still

informative

There are

significant

problems

with the

scale

design

Table 2: Case Processing Summary.

N %

Cases Vali

175 100.0

Exclude

0 0

Total 175 100.0

a. Listwise deletion based on all variables in the

procedure.

Table 3: Reliability Statistics.

Cronbach's Alpha N of Items

.801 6

The reliability test of the questionnaire was 0.801,

which is between 0.9 and 0.8 indicating that the

intrinsic reliability of the questionnaire is acceptable.

3 EMPIRICAL ANALYSIS

3.1 Theoretical Framework

Cheng (2006) argues that the beginnings of

behavioral decision theory began with the Allais

Paradox and the Edwards Paradox, which were

developed in response to problems that were difficult

to be solved by rational decision theory in an

alternative way. Domestic scholar Tang (2013) used

a logistic model and Blinder-Oaxaca decomposition

to argue that the income situation of farm households,

the education level of the head of household and the

level of household labor are the main influencing

factors that affect farm households' production

decisions. Li (2016) concluded that under the

conditions of semi-closed markets, consumption

ICEMME 2022 - The International Conference on Economic Management and Model Engineering

factors have a significant impact on the production

decisions of farm households. Lu (2015) considered

the lower decision rate of farmers with low land

utilization, poor land resources and livelihood

conditions for agribusiness investment under

agribusiness investment conditions. Song (2015) in

analyzing the production decision-making behavior

of farm households found that whether farmers make

rational decisions is influenced by the personal

characteristics of decision makers, farm household

characteristics, market factors, risk attitudes of

decision makers, and the ability to use market

information.

3.2 Index System Construction

A total of nine variables were introduced in this paper

for the empirical analysis of the factors influencing

farmers' production decisions, and the implementable

significance of the variables is detailed in Table 4.

Table 4: Definition of implementability of explanatory variables.

Variable Name Definition of variables

Dependent variable (Y)

Agricultural production inputs

e of head of househol

1=increase; 2=no change/decrease

Inde

endent variable

(

)

e of head of househol

Actual a

e of the head of the farm household

ears

)

Years of education of the head of

househol

Years of cultural education received by the head of household

ears

)

Farmers'

rowin

erience Time farmers have been en

ed in farmin

ears

)

Size of household labor force Number of laborers in households engaged in vegetable farming

ersons

)

Farmers' arable land surface Area of land used b

farmers for cro

cultivation

(

)

Annual income status of farm

households

Annual income of farmers from vegetable cultivation (million

yuan)

Farmers' risk attitude 0=risk aversion/neutral; 1=risk a

etite

Farmers' attention to market price

information

1=basically not; 2=occasionally/generally; 3=often

3.3 Model Setting

The dependent variable of this study is the production

decision behavior of farm households, while the

independent variables are the factors influencing

including farm household endowment (age of the

household head, years of education of the household

head, farming experience, size of household labor

force, arable land area, and income status of the farm

household), risk attitude of the farm household (risk

aversion, risk neutrality, and risk preference), and

market price information of the crop. Logistic binary

logistic regression models were constructed to study

the effects of the above three major variables on farm

households' production decisions in the following

form.

)

ln()logit(

−

(1)

rewrite the form as:

()

+++++=

8822110

Plogit XXX 

(2)

is the constant term of the model,

8-1

The

coefficients of the age of the household head, years of

education of the household head, experience the

household head in farming, annual income of the

farming household, size of the household labor force,

area of land under cultivation, risk attitude of the

farming household and attention of the farming

household to market price information, respectively,

is the random error term.

3.4 Logistic Regression Results of

Variables

The variables X1-X6 represent the age of the

household head, the years of education of the

household head, the farming experience of the farmer,

the income status of the farmer, the number of

household laborers, and the area of land cultivated,

respectively. First of all, the sig value of the constant

is 0 as can be seen from the variables in the equation

in Table 5, indicating that the constant is significant.

Table 6 is a test of the goodness of fit of the model,

and the result shows that the sig value of 0.648 is

greater than 0.05 indicating that the model fits well

and there is no significant difference.

The Inﬂuencing Factors of Farming Households’ Production Decisions

Table 5: Variables in the Equation.

B S.E. Wal

df Si

. Ex

)

Step 0

Constant

1.536 .198 60.169 1 .000 4.645

Table 6: Hosmer and Lemeshow Test.

Ste

Chi-s

uare df Si

5.997 8 648

Table 7: Variables in the Equation

B S.E. Wal

df Si

. Ex

)

Step 1

Age

.055 5.019E3 .000 1 1.000 1.057

Years of

education

.545 .123 19.613 1 .000 .580

Planting

experience

.098 5.019E3 .000 1 1.000 1.103

Arable land

area

.576 .355 2.630 1 .007 .562

Family Labor

.103 .973 .011 1 .916 1.108

Agricultural

income

.149 .378 .156 1 .005 1.161

Risk attitude

20.327 2.062E4 6.005 1 .044 .035

Market price

information

concern

-1.215 .768 2.503 1 .014 .297

Constant

1.604 9.034E4 .000 1 0.002 4.975

a. Variable(s) entered on step 1: Age, years of education, farming experience, acreage, family labor, farm income, risk attitude,

and attention to market price information.

3.5 Analysis of Factors Influencing

Farmers' Production Decisions

from the Empirical Results

According to the analysis of the binary logistic

regression results in the previous chapter, the

influencing factors: years of education of the head of

household, income status of the farmer, risk attitude

of the farmer and arable land area have significant

effects on agricultural production inputs. The rest

failed the significance test, and their effects on

agricultural production inputs were not significant.

(1) Years of education of the household head

The influence of the number of years of education

of the household head on the production decision of

the farming household is positive. More years of

education means that farmers are highly educated,

open-minded and not conservative, and more

receptive to new varieties and technologies, and tend

to take into account all the available information

around them to make a decision that is conducive to

increasing their income. On the contrary, farmers

with less years of education or even no education are

more conservative in their decision making and can

hardly accept the advanced agricultural business

model, and can only follow the previous year's

planting decision unchanged.

(2) Farmers' income

Farmers' income has a significant positive

influence on agricultural production inputs. When

considering increasing agricultural production inputs,

farmers tend to consider the adequacy of the capital

ICEMME 2022 - The International Conference on Economic Management and Model Engineering

they need to invest. When their income situation is

not satisfactory, they will reduce their inputs in order

to prevent their income from falling short of their

income. Farmers with good incomes are bold in their

production inputs, which helps them to make positive

decisions to increase their production income.

(3) Arable land area

The area of land under cultivation has a positive

effect on farmers' production decisions. Farmers with

more acreage have more choices in what to produce

and are more willing to increase agricultural

production inputs than those with small-scale land,

which is more conducive to making decisions that are

conducive to increasing income.

(4) Farmers' risk attitudes

Farmers' risk attitude has a significant influence

on how farmers make agricultural production inputs,

the more risk attitude is preferred the more they can

increase production inputs and make reasonable

production decisions; when risk attitude is avoided or

risk-neutral it means that farmers' decisions are more

conservative, which is not conducive to making

decisions to increase agricultural income.

In summary, the most significant factor in

farmers' endowment on production decision is the

years of education of the head of household, followed

by farm income and arable land area; the level of

influence of farmers' risk attitude on production

decision is lower than that of farmers' endowment,

and the influence of farmers' attention to market price

information on production decision is not significant.

4 SUGGESTIONS FOR

OPTIMIZING FARMERS'

PRODUCTION DECISIONS

4.1 Strengthen the Construction of

Rural Culture

From the empirical analysis, it is clear that the

influence of the number of years of education of the

household head on the production decision is very

significant. Farmers with fewer years of education

and lower literacy levels have limited factors to

consider in decision-making and are unable to make

decisions that are beneficial to increasing income. In

order to improve the economic strength of rural areas,

the first step is to use information technology to

promote cultural construction in rural areas, provide

quality cultural activities, and improve the overall

quality of farmers. By continuously learning

advanced planting knowledge and planting methods,

farmers can improve their knowledge and make

reasonable decisions that are conducive to increasing

their income.

4.2 Improve Government Subsidy

Policy

Improve the government subsidy policy so that

farmers can receive subsidies in case they encounter

such risks as climate change, pests and diseases and

are unable to recover the losses by themselves. This

will help motivate farmers to plant and increase their

income, and also provide a reliable aid for farmers to

make reasonable decisions.

4.3 Increasing the Productivity of the

Land

Arable land size also has a positive effect on farmers'

production decisions. Farmers who own more arable

land have more options. To increase income, the

productivity of the land must be increased. With

technical support from the government, the land

should be properly fertilized, irrigated and pesticides

applied to reduce the damage to the land and maintain

the fertility of the soil. The fertility of the soil can also

be increased by returning straw to the fields or by

applying more organic fertilizers such as farmyard

manure to increase the productivity of the land, thus

increasing the yield per unit of crops and increasing

the income of farmers and facilitating them to make

more rational decisions for the next production.

4.4 Improve Rural Social Security

System

First, strengthen market management and provide

farmers with a stable sales market. The market self-

regulation has the disadvantages of spontaneity,

blindness and lagging, which requires the government

to supervise and manage the market players and their

behavior according to the law. The government

functions to regulate the operation of the national

economy to improve the market mechanism, unify

pricing, strengthen the management of external

supply, and reduce the losses of farmers caused by

significant price reductions.

Second, provide comprehensive agricultural

insurance services to enhance farmers' confidence in

planting. Since farmers cannot afford alone the loss

of not receiving the expected income after increasing

production inputs. Therefore, it is necessary for the

government to provide preferential agricultural

insurance services to farmers and include all risks that

The Inﬂuencing Factors of Farming Households’ Production Decisions

may cause losses to farmers' income as part of the

insurance, in order to reduce farmers' losses, increase

farmers' confidence in planting, and promote farmers'

active decision-making.

5 CONCLUSION

This study mainly obtained first-hand data for the

study by distributing questionnaires and in-depth

interviews. The data were summarized using Excel

software, and the factors affecting farmers'

production decisions (farmers' endowment, farmers'

risk attitude, and market information) were analyzed

empirically using SPSS statistical software and the

logistic model. The higher the number of years of

education, the more educated the farmer is and the

more able he or she is to make reasonable decisions.

Farmers' risk attitudes also have a significant

influence on production decisions. When farmers'

risk attitudes focus on risk neutrality and risk

avoidance, this can lead to negative decision-making

and is not conducive to increasing farm income.

The surveyed farmers have the habit of paying

attention to market price information, but their ability

to use market price information for decision-making

is still lacking. If farmers can make full use of market

price information to make decisions, this will greatly

reduce farmers' production losses and increase their

farm income.

ACKNOWLEDGMENT

National Natural Science Foundation of China Youth

Fund Project "Research on Technology Innovation

and Productivity Enhancement of Real Economy

from the Perspective of Financialization" (71703012);

Liaoning Provincial Department of Education Basic

Research Project for Higher Education (Key Project)

"Economic Uncertainty, Financialization of Real

Economy and Capital Allocation Efficiency"

(J202106); Dalian Science and Technology

Innovation Think Tank Project of Dalian Association

of Science and Technology, "Research on

Countermeasures to Enhance Innovation Capability

of Dalian Manufacturing Enterprises Driven by

Digital Transformation" (DLKX2021B07); Dalian

Academy of Social Sciences (Research Center)

Project 2021 "Research on Countermeasures for

High-Quality Development of Dalian Equipment

Manufacturing Enterprises in the Context of Digital

Transformation" (2021dlsky068); Dalian Academy

of Social Sciences (Research Center) 2022 Think

Tank Research Base Project "Research on the Path of

High-Quality Development of Dalian's Health and

Pension Industry" (2022dlskyjd020).

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ICEMME 2022 - The International Conference on Economic Management and Model Engineering