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Sustaining farmers life affected by the naturaldisaster; an implication during the covid-19pandemicMuhammad Basir Cyio ( basircyio@yahoo.com )
Tadulako University: Universitas Tadulako https://orcid.org/0000-0002-0334-5106Alam Anshary
Tadulako University: Universitas TadulakoMahfudz Mahfudz
Tadulako University: Universitas TadulakoIsrun Isrun
Tadulako University: Universitas TadulakoMery Napitupulu
Tadulako University: Universitas TadulakoFadhliah Fadhliah
Tadulako University: Universitas Tadulako
Research Article
Keywords: Covid-19, earthquake, farmers, liquefaction
Posted Date: February 22nd, 2021
DOI: https://doi.org/10.21203/rs.3.rs-263089/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Sustaining farmers life affected by the natural disaster; 1
an implication during the covid-19 pandemic 2
Muhammad Basir-Cyio1*), Alam Anshary1), Mahfudz1), Isrun1), Mery Napitupulu2), 3
Fadhliah3) 4
1Department of Agroecotechnology, Faculty of Agriculture, Tadulako University, Jl. 5
Soekarno-Hatta Km 09, Palu 94118, Indonesia 6
2Department of Natural Education Science, Faculty of Teacher Training and Education 7
Science, Tadulako University, Jl. Soekarno-Hatta Km 09, Palu 94118, Indonesia. 8
3Department of Communication Science, Faculty of Social and Political Science, Tadulako 9
University, Jl. Soekarno-Hatta Km 09, Palu 94118, Indonesia 10
11
Corresponding Author (basircyio@yahoo.com & basircyio@untad.ac.id) 12
13
14
ABSTRACT 15
16
Purpose This study aims to measure two investigate and identify effect of treatment factors: 17
Organic Material and Triple Super Phosphate Fertilizer and also to study four main 18
components: Extension (X1), Agricultural Production Facilities (X2), Government Assistance 19
(X3), and Covid-19 dissemination (X4) information. 20
Materials and methods The data collected through surveys and interviews include; (i) soil 21
characteristics data and (ii) socio-economic conditions of farmers affected by the disaster and 22
the Covid-19 pandemic. The statistical analysis used is Principal Component Analysis (PCA) 23
using the Paleontological Statistics (PAST) application. 24
Result and Discussion The result showed the M3 P3 treatment combination gave the highest 25
effect: pH-H2O (6.13), P-total (52.28 mg/100g), P-available (28.62 ppm), plant dry weight 26
(713.75 g/plot), P-uptake (0.58 g/plant), and Corncob (6.2 t/ha). The main component, the most 27
effective factor to the successive variation X2 (PC-1) 75.55%; X3 (PC-2) 18.22%; and X4 (PC-28
3) 3.94%, while X1 (PC-4) did not result in variation (2.28%). The probability analysis showed 29
farmers post-Earthquake income was affected by production facilities provisions (X2) p (0.015) 30
< ∝(0.05) and Government aid (X3) p (0.035) <∝(0.05) and amidst pandemic (X3) p (0.028) 31
<∝ (0.05) by government aid, with Correlation Value of each Jono Oge (R) 0.91 and Toaya 32
(R) 0.64, indicating main component factor in Jono Oge is higher at (0.91) since land was 33
displaced by Liquefaction while (0.64) in Toaya—the ground failure was by Earthquake. 34
35
Keywords: Covid-19; earthquake; farmers; liquefaction. 36
37
38
INTRODUCTION 39
40
The earthquake block displacement and Liquefaction in September 2018 in Central 41
Sulawesi Province, Republic of Indonesia, destroyed various aspects of community life and 42
significantly impacted farmers' lives in Sigi and Donggala Regency, where the high-impact 43
earthquake and liquefaction centers occurred. Farmland economic productivity has 44
deteriorated, farmer purchasing power has decreased, and their farmland remains unworked 45
because it is technically impossible to cultivate. Before the living conditions of farmers were 46
restored, the Covid-19 pandemic hit the world, including Indonesia. Jono Oge, Sigi Regency, 47
Petobo Palu City, and Toaya Donggala Regency, Central Sulawesi Province, are the areas that 48
have been most severely affected by natural disasters. This condition worsens the productivity 49
of agricultural areas affected by the earthquake and liquefaction since, in addition to no 50
production facilities and limited government assistance, there is no available counseling, and 51
the massive Covid-19 information has unfavorable implications for the psychosocial aspects 52
of farmers (List, 2020; Luciano, 2020). The difficulty to restore the productivity of damaged 53
land is intensified because it requires capital for land clearing. Further, farmers' enthusiasm has 54
decreased due to the compounded impacts of natural disasters and the Covid-19 pandemic. The 55
community's economic condition has experienced a drastic decline, affecting farmers' 56
purchasing power; they do not have enough money to send their children to higher education. 57
The helplessness of farmers forced them to decide to stop the continuation of their children's 58
education, which led to an increase in school dropouts (Ahsanuzzaman and Islam, 2020; 59
Nurhadi et al., 2019). 60
There are many factors why farmers cannot maintain their socio-economic life. It is 61
difficult to obtain recovery funds, there is an absence of production facilities and supporting 62
elements such as fertilizers, superior seeds and agronomic extension of technique and skill 63
knowledge. Although, the government continues providing temporary housing and permanent 64
housing, there is an insufficient attention to the farming community by local and central 65
governments. Farmers' land ownership status is also unclear because of the shift in property 66
boundaries after liquefaction. In stormy conditions at lowland farms, soils are no longer arable. 67
This problem requires the full involvement of the government so that farmers gain certainty 68
from the legal aspect. 69
When farmers were in a horrible and poor condition, they lost their enthusiasm for life. 70
Responding to the Covid-19 outbreak, the central and local governments issued a Lockdown 71
policy. All people were prohibited from doing activities outside the home to prevent 72
transmission of Covid-19 (Liu et al., 2020; Wammes et al., 2020). This policy has added to 73
farmers' living burden, which is getting worse because they have no option to farming activities 74
on farmland affected by liquefaction. 75
In traumatic conditions due to earthquakes and liquefaction, information about the 76
ferocity of Covid-19 as a deadly virus also adds to farmers and their families fear. Coverage 77
on social media of the number of positive-test people suffering from the coronavirus and the 78
number of deaths that continue to increase daily has increased anxiety and farmers' fear. In a 79
crisis situation, government assistance is the only way to save farmer households, which 80
generally have three to four family members. The worldwide news spread of the Covid-19 81
pandemic is frightening for farmers maintaining their farms, which are their only source of 82
livelihood to meet their daily needs (Dupas et al., 2020; Su et al., 2020). Pervasive and fast 83
information across all parts of the world on the Covid-19 pandemic is sourced from both 84
accountable official media and information that is false or a hoax (Rollett et al., 2020; Zhong 85
et al., 2020). This insufficient information directly affects the various activities of family 86
farmers who generally have low levels of education. Their spirit of work immediately dropped, 87
life was full of pain and uncertainty, and even more, several farmers had to accept the fact a 88
family member was declared Covid-19 active after an initial examination of suspected 89
symptoms. 90
The news reported by gisanddata.maps.arcgis.com about the number of deaths due to 91
Covid-19 has further added to the fears by farmers who have not yet recovered from the 92
earthquake and liquefaction. At the time of this writing, as many as 82,119 people have died, 93
and it is stated the total is increasing from day to day from information that spreads very fast. 94
The spread of Covid-19 had hit 184 countries, including China and patients from the Diamond 95
Princess Cruise Ship. Information on the largest number of deaths was reported in Italy, which 96
reached 17,127 people, followed by Spain with 14,045 people, and the United States with 97
12,895 deaths. In France, the number of sufferers who died was around 10,328, while it reached 98
6,159 people in England. The number of Covid-19 cases continues to increase day by day; even 99
until mid-April 2020, the mild cases of Covid-19 have reached 1,015,403, according to data 100
released by the Johns Hopkins University Center for Systems Science and Engineering (CSSE). 101
The Indonesian government has understood the destructive impacts upon communities 102
due to the implementation of the Covid-19 emergency conditions, especially upon farmers 103
affected by the earthquake and liquefaction. Limited lockdown implementation is expected to 104
provide farmers with opportunities to return to their activities by paying attention to Health 105
protocol, especially maintaining distance and wearing masks when leaving the house. In mid-106
April 2020, cases of Covid-19, which infected humans reached 1,528 patients and resulted in 107
a mortality ratio of 8.9%, meaning that every 100 people who are positively infected, an 108
average of 8.9 people die. Data released by the Task Force for the Acceleration of Handling 109
(Indonesia Center for Covid-19 Pandemic) reported that the number of patients who were 110
positively infected with Covid-19 reached 1,528 people, 81of those patients were declared 111
cured while 136 people died (Cervantes‐Arslanian et al., 2020). The increase in the number of 112
infected, dead, and recovered cases has dominated coverage in various printed, electronic, and 113
social media. Information traffic about the Covid-19 case affects people's lives, both personally 114
and communally, which impacts productivity, social conditions (morale), and household 115
economy (Nwanaji-Enwerem et al., 2020; Olsen et al., 2020). The suffering and helplessness 116
of farmers affected by the earthquake and Liquefaction in Central Sulawesi Province are of 117
increasing concern. People affected by natural disasters generally experience traumatic 118
reactions and feelings of insecurity (Rajindra et al., 2020). In July 2020, the number of Covid-119
19 cases reached 3,963, and those suspected had reached 100,236. Symptoms used as a suspect 120
indicator are respiratory problems and a history of contact with the patient (Bakar et al., 2020; 121
Tosepu et al., 2020). 122
123
People living in rural areas, especially farmers affected by the earthquake and Liquefaction 124
around Jono Oge, Sigi Regency, Petobo area, Palu City, and Toaya, Central Sulawesi Province, 125
experienced prolonged suffering. Their agricultural products, which have been canceled 126
through intermediary traders for sale to markets in the city, are also hampered by the 127
government's policy of implementing lockdowns to reduce human contact through the 128
application of "Social Distancing." Two situations and conditions that affect farmer families' 129
lives are traumatic due to the natural disaster in September 2018 and the massive Covid-19 130
Pandemic information. There is a lack of assistance and facilities that can be accessed by 131
farmers. The strength of Covid-19 pandemic information is difficult to contain, especially 132
issues related to the number of people who have been infected and died (Ulhaq and Soraya, 133
2020; Yanti et al., 2020). 134
Based on the background description, this study aims to determine the main 135
components of the variable factor (i) Extension; (ii) Agricultural Production Facilities, (iii) 136
Government Aid; and, (iv) Information on the distribution of Covid-19 to farmers. Also, to 137
assess the condition of land health and treatment in the form of organic material (Goat Manure) 138
and TSP. Farmers' income after natural disasters and continuing with the Covid-19 pandemic 139
will also be analyzed. 140
141
METHODS 142
143
Study area 144
The area affected by the earthquake and liquefaction was around 436.87 ha, as indicated 145
by the Humanitarian OpenStreetMap mapping (OSM, 2018). Jono Oge and Petobo are areas 146
affected by natural disasters from earthquakes and liquefaction, so that farmers who live and 147
work on agricultural land in these two areas are not yet stable from psychosocial and economic 148
aspects. Around 603 people or about 112 heads of families residing in the area are in an inferior 149
socio-economic condition (Figure 1). Government assistance is minimal, access is minimal, 150
and news of deaths caused by Covid-19 is massive on social media. The people of Toaya, 151
Donggala Regency, who were affected by the earthquake and information on the Covid-19 152
pandemic, were also experiencing the same condition. 153
154
155
Figure 1. Research locations in Jono Oge, Sigi and Toaya Regencies Donggala, Central 156
Sulawesi Province, Indonesia 157
Data Collection 158
159
Population and Sample 160
161
This study's total population was all farmers affected by the earthquake and 162
liquefaction, estimated at not less than 112 heads of families or around 603 people with a range 163
of 2 to 5 family members. The minimum sample size for the study of the population of heads 164
of household is determined using the Slovin sample calculation, Margin of error 5%, with the 165
following formula: 166 𝑛𝑛 =𝑁𝑁
{1 + (𝑁𝑁. 𝑒𝑒2)} 167
168
Based on the Solvin formulation, the number of research samples was 88 heads of households 169
divided into five age groups, namely (i) <= 20 years, (ii) 21-30 years, (iii) 31-40 years, (iv) 41-170
50 years, and (v) > = 51 years. Based on the age groups as subsamples, it is a purposive sample, 171
representative of the population. 172
Data Analysis 173
The independent variable data includes X1 = Extension (extension); X2 = Agricultural 174
Production Facilities (Production Facilities); X3 = Government Aid; and X4 = Covid-19 175
Information. The dependent variables include Y1 = Income During Covid-19 Pandemic 176
(Income During the Covid-19 Pandemic); Y2 = Income a Year After the Earthquake; Y3 = 177
income After the Earthquake, apart from the initial data on soil health conditions and the effect 178
of organic matter treatment (Goat Manure) and TSP on several soil chemical properties and 179
weight of corn cobs. 180
The data collected through surveys and interviews include (i) soil characteristics data 181
and (ii) socio-economic conditions of farmers affected by the disaster and the Covid-19 182
pandemic. The statistical analysis used is Principal Component Analysis (PCA) using the 183
Paleontological Statistics (PAST) application. This analysis is used to determine the most 184
dominant factor and to reduce data to be easier to interpret. The principal component analysis 185
is a middle analysis of a research process before proceeding to the General Linear Model 186
(GLM) analysis with the help of R-Studio statistical and graphics tool, to obtain equations that 187
are not normally distributed. Generalized Linear Models (GLM) is a regression analysis whose 188
response is one of the exponential parts determining the relationship between the dependent 189
and dependent variables. GLM is developing an ordinary regression model that includes the 190
dependent variable with an abnormal distribution and the mean model function. The 191
distribution function accommodated by GLM is a distribution that is included in the 192
exponential, such as the binomial, standard, Poisson, gamma, and exponential distribution. 193
194
For the response variable, Generalized Linear Models (GLM) are. 195
196 𝑓𝑓 (𝑦𝑦) = 𝑐𝑐(𝑦𝑦,∅)𝑒𝑒𝑒𝑒𝑒𝑒 {𝑦𝑦𝑦𝑦−𝛼𝛼(𝑦𝑦)𝑦𝑦 } 197
𝑔𝑔 (𝜇𝜇)= 𝑒𝑒′𝛽𝛽 198
𝑓𝑓 ( ) = the probability function for the response variable 𝑌𝑌 which has an exponential 199
distribution 200 𝑔𝑔 ( ) = link function or linearly related to the explanatory variable in 𝑒𝑒 201
202
There are three main components in GLM (McCullagh and Nelder, 1989): 203
a. The random component, namely the element of 𝑌𝑌 which is free and the 204
probability distribution function 𝑌𝑌 belongs to the exponential distribution 205
family with E(Y) = µ 206
b. Systematic components, namely X1, X2, ...., Xp which produce a linear 207
estimator η where η = β0 + β1 X1 + ... + β p X p 208
c. The link function g (μ), Describes the relationship between the linear estimator 209
η and the mean μ. This relationship can be written as η = g (μ). This function is 210
another monotonous function. 211
Exponential Family Distribution. The equation (𝑦𝑦) in the above discussion is a general form of 212
the probability function of the dependent variable 𝑌𝑌 in GLM where 𝜃𝜃 and 𝜙𝜙 are canonical 213
parameters and dispersion parameters, respectively. 214
A distribution whose probability function can be written as the equation (𝑦𝑦), then the 215
distribution belongs to the exponential family distribution. Canonical Links and 216
Links. The link function of the exponential family distribution that can be used 217
in Generalized Linear Models (GLM) for the current data is the Binomial distribution using 218
Link Logit with 219
𝜇𝜇 = 𝑙𝑙𝑛𝑛 𝜇𝜇1 − 𝜇𝜇 220
RESULTS AND DISCUSSIONS 221
Characteristics of Agricultural Soil & Effect of Treatment on Indicators 222
223
The condition of agricultural land affected by natural liquefaction has been analyzed at the 224
Laboratory of Environment and Land Resources, Faculty of Agriculture, Tadulako University, 225
in March 2020. The results of the analysis of soil characteristics are presented in Table 1. 226
227
Table 1. Characteristics of agricultural land affected by liquefaction 228
No. Soil Parameter Unit Result Criteria
1 Sand % 60.79
2 Loam % 10.15 Loamy Clay Sand
3 Clay % 28.78
5 Bulk Density g/cm3 1.61
7 C-organic % 1.07 Low
8 N-Total % 0.16 Low
9 C/N-rasio
8.49 Low
10 pH H2O (1:2.5) 5.78 Moderate Acid
11 pH KCl (1:2.5) 4.90
12 P2O5 (HCl 25%) mg/100g 24.06 Low
13 P2O5 (Bray I) Ppm 10.22 Low
14 K2O (HCl 25%) me/100g 29.32 Medium
15 Ca me/100g 4.72 Low
16 Mg me/100g 0.41 Low
17 K me/100g 0.25 Low
18 Na me/100g 0.19 Low
19 CEC me/100g 23.25 Medium
20 BS % 23.62 Low
21 Al-exchangeble me/100g 0.37
Source: Soil Sample Analysis at Laboratory of Agroecotechnology Faculty of Agriculture, 229
Tadulako University (2020) 230
The results of the analysis of several soil variables generally show at a low category. 231
Such conditions require treatment to improve fertility and soil health conditions, both 232
physically and chemically (Mosquera-Losada et al., 2019; Xu et al., 2020). Technically, land 233
conditions affected by liquefaction can be planted although only to a limited scale. The type of 234
plant that can be cultivated by considering the level of soil fertility is corn to help meet farmers' 235
food needs so that they can survive. However, agricultural activities did not continue due to 236
the issuance of government policies for Stay at Home (SAH) and Work from Home (WFH) 237
due to the Covid-19 Pandemic, even though the living conditions of farmers have not been 238
stable since the earthquake and liquefaction on 28 September 2018 which led to declined 239
agricultural businesses. They experienced total crop failure. In efforts to improve the physical 240
and chemical properties of soil affected by the earthquake and liquefaction, two treatments 241
were used, namely Organic Fertilizer (Goat Manure) and Triple Super Phosphate (TSP) to 242
improve pH-H2O, P-total, P-available, dry weight plant tissue, P-uptake, and Corncob Weight 243
(Table 2). To determine the level of closeness between variables, the Correlation (R) test was 244
performed, as presented in Table 3. 245
246
Table 2. Effect of Organic Fertilizer (Goat Manure) and TSP on Observer Variables 247
F-calculated
Sources of Variants
Group Goat Manure
(M)
Triple Super
Phosphate
(P)
Interaction
(MSP)
pH-H2O 0.46 4395.122** 1779.937** 15.593**
P-total (mg/100-g soil 0.968 2497.718** 1179.111** 75.633**
P-available (ppm) 2.337 2097842.601** 769616.471** 34840.041**
Dry weight plant tissue
(g plot-1) 0.625 8497.672** 4638.908** 201.083**
P-uptake (mg Kg-1) 0.277 419.778** 242.821** 2.790**
Corncob Weight (g plot-
1) 0.545 101.142** 147.143** 2.812**
**. Treatment is significant at the 0.01 level (2-tailed). 248
249
Table 2 shows the organic matter (Goat Manure) and TSP treatment has a very 250
significant effect on all soil elements, starting from pH-H2O, P-total, P-available, P-uptake, dry 251
tissue weight, to corncob weight, both the single effect of each treatment (OM & TSP) as well 252
as the interaction effect (Basir-Cyio et al., 2021). The high response of soil and plants to 253
treatment indicates that the soil is in poor nutrient and unhealthy conditions. The highest effect 254
of BO and TSP treatment was obtained in combination (M3P3) for all dependent variables, 255
namely (i) pH-H2O (6.13), (ii) P-total (52.28 mg/100g), (iii) P-available (28.62 ppm), (iv) plant 256
dry weight (713.75 g/plot), (v) P-uptake (0.58 g/plant), and (vi) Corncob (6.2 t/ha). Based on 257
these data, it appears that the higher the dose of organic matter (Manure) and TSP applied, the 258
higher the effect on all observed variables. The responses of all dependent variables given the 259
treatment showed that the soil condition needed external input, synthetic fertilizers, and organic 260
fertilizers (J. Li et al., 2020; Song et al., 2020; Xie et al., 2020). The association between 261
variables has been analyzed by calculating the Correlation value (R), as presented in Table 3. 262
Table 3. Correlation Value between Variables that Indicate the Level of Closeness 263
Variable Indicators pH-H2O P-Total P-Available P-Uptake Corncob
Weight
pH-H2O
Pearson Correlation 1 0.927** 0.921** 0.961** 0.905**
Sig. (2-tailed) 0.000 0.000 0.000 0.000
N 48 48 48 48 48
P-Total
Pearson Correlation .0927** 1 0.971** 0.967** 0.905**
Sig. (2-tailed) 0.000 0.000 0.000 0.000
N 48 48 48 48 48
P-Available
Pearson Correlation 0.921** 0.971** 1 0.959** 0.880**
Sig. (2-tailed) 0.000 0.000 0.000 0.000
N 48 48 48 48 48
P-Uptake
Pearson Correlation 0.961** 0.967** 0.959** 1 0.918**
Sig. (2-tailed) 0.000 0.000 0.000 0.000
N 48 48 48 48 48
Corncob
Weight
Pearson Correlation 0.905** 0.905** 0.880** 0.918** 1
Sig. (2-tailed) 0.000 0.000 0.000 0.000
N 48 48 48 48 48
**. Correlation is significant at the 0.01 level (2-tailed).
264
From all observations, the correlation value (R) ranges from 0.88 to 0.97, a correlation 265
that is at an extreme level of closeness. The story of similarity above 0.80 indicates that each 266
variable affects other variables or, in other words, has the same position so that it does not 267
allow any factors to be determined as the main component (Gilbert et al., 2020; Huang et al., 268
2020). Agricultural land affected by the earthquake and liquefaction, in addition to 269
experiencing physical damage, acquires chemical damage (Basir-Cyio et al., 2020; Nakagawa 270
et al., 2020), so that a negative impact on nutrient availability, decreases the carrying capacity 271
of agricultural productivity (Lankoski and Thiem, 2020; Singh et al., 2018). 272
273
Determination of Main Component Variables 274
Before analyzing the main components of the factors that have been determined in the 275
study, multicollinearity testing was carried out to determine the relationship between 276
independent variables before choosing further testing using Principal Component Analysis 277
(PCA). The results of the collinearity test are presented in Table 4. 278
Table 4. Multicollinearity test results between independent variables 279
Independent Variable Multicollinearity
Estimate SE Tolerance VIF
Intercept 1,500 0,258
The extension (X1) -0,500 0,089 0,183 5,461
Agricultural Prod. Facilities (X2) 0,500 0,094 0,160 6,234
Government Aid (X3) 1,691E-015 0,081 0,249 4,002
Covid-19 Information (X4) -1037E-015 0,079 0,308 3,250
280
From the test results, it is known that all Variance Inflation Factors (VIF) values for the 281
four variables are less than 10, which means that there is no relationship between the 282
independent variables, or it is assumed that multicollinearity does not occur. The matrix 283
between the independent variables and the principal components is presented in Table 5. 284
285
286
Table 5.Independent Variable Matrix with Main Components (PC) 287
Variable Principal Component (PC)
Eigenvalue % Variance PC-1 PC-2 PC-3 PC-4
Penyuluhan (X1) -0.498 0.531 0.176 0.663 0.0910 2.280
Sarana Produksi (X2) 0.518 -0.439 0.031 0.733 3.0221 75.553
Bantuan Pemerintah (X3) 0.510 0.413 0.740 -0.144 0.7286 18.215
288
The four variables studied after the factor analysis was carried out with the principal 289
component analysis method (PCA), there were two variables as the main components, namely 290
the first factor (F1) was production facilities (X2). The second factor (F2) was government 291
assistance (X3), while a supporting component is the third factor (F3), namely Covid-19 (X4) 292
information. The first factor has an eigenvalue of 3.022, which can explain the total diversity 293
of 75.55%. In comparison, the second factor (F2) has an eigenvalue of 0.7285, which can 294
explain the whole variety of 18.215%. The third factor (F3) Covid-19 information with an 295
eigenvalue value of 0.1577 can only describe the total diversity of 3.94%. The fourth factor 296
(F4) as an additional factor, namely Agricultural Extension (X1), was not used in the analysis 297
because it only had an eigenvalue of 0.091 which was only able to explain the total diversity 298
of 2.28%. The PCA test results for all variables are presented in Figure 2. 299
300
301
Figure 2. Plotting of the dominant main components, namely PC-1 (X2) Agricultural 302
Production Facilities, PC-2 (X3) Government Assistance, and Supporting Components, namely 303
PC-3 (X4) Covid-19 Information to Farmers. 304
305
Figure 2 shows that of the two locations affected by the earthquake, namely in Toaya, 306
Donggala Regency and land impacted by Liquefaction in Jono Oge, Sigi Regency, the 307
dominant main components are agricultural production facilities (X2) and government 308
assistance (X3) while Covid-19 Information (X4) enter the main element that is supporting. 309
Covid-19 Information (X4) 0.473 0.595 -0.648 0.051 0.1577 3.940
This shows that when there is a disruption in the production process due to natural disasters, 310
farmers need production facilities and government assistance because of the urgent condition 311
(Chapagain and Raizada, 2017; Liesivaara and Myyrä, 2017; Peng et al., 2020). The scarcity 312
of agricultural production facilities is due to the unavailability of transport modes, making it 313
challenging to deliver production facilities from Java, from where production facilities are 314
made and distributed to areas where agricultural land is affected by natural disasters and 315
liquefaction. The ongoing impact of natural disasters and liquefaction is getting worse due to 316
the Covid-19 Pandemic response minimizing the mobility of goods and people. This limitation 317
has a broad impact on the availability of production facilities to hampered agricultural 318
cultivation (Islam et al., 2020; Klomp and Hoogezand, 2018). The massive Covid-19 pandemic 319
information has helped limit transactions during difficult situations after the government issued 320
lockdown in several areas. Government assistance is the only solution which farmers expect 321
will meet farmers' basic needs when their agricultural businesses experience degradation due 322
to the earthquake, liquefaction, and the Covid-19 pandemic. Information on the massive 323
dangers of Covid-19 to society has created a sense of fear and is traumatic to farmers and their 324
families in being prevented from normally run activities, (Boyraz et al., 2020; Trnka and 325
Lorencova, 2020). Their efforts are limited to only growing vegetables in their yard due to the 326
prohibition on leaving the house. The helplessness of farmers in dealing with the effects of the 327
earthquake, liquefaction, and the Covid-19 pandemic has caused many of their children to drop 328
out of school. The declining purchasing power factor and the high psychological burden 329
experienced by farmers have reduced their working spirit, (Heo et al., 2020; Soron, 2019), so 330
that to increase their confidence, government support and guidance from academics are needed 331
(Balezentis et al., 2020; Wu, 2020). To strengthen the prediction of the strength of the Main 332
Component (PC) in describing each factor's diversity, the sequence of powers PC-1 to PC-4 is 333
presented in the analysis of the residual diagnostic plot in Figure 3. 334
335
Figure 3. The PC sequence diagnostic plot is most vital in capturing the diversity 336
of factors. 337
338
Figure 3 shows a diagnostic plot of the PCA examination results that functioned 339
properly on the data. The main components are made in a sequence of variations ranging from 340
PC-1 to PC-4. The series is based on the number of variations produced and, at the same time, 341
describes the amount of information captured by the Principal Component. PC-1, PC-2, and 342
PC-3 illustrating the considerable variation produced so that the next PC can be ignored. The 343
scree plot shows how much variation each PC captures from the data. The y-axis is an 344
eigenvalue, which means the number of variations. The agricultural production facility factor 345
(X2) is the highest main component (PC-1) because the land affected by the earthquake and 346
liquefaction is in sterile conditions, and production facilities, especially fertilizers, extensively 347
determine agricultural business (Chamberlain and Anseeuw, 2019; Dhulipala and Patil, 2020). 348
The scree plot strengthens the main component, which has a determinant value. The gloomier 349
the curve, the more ideal it is to form a point of intersection with the scrub line. Elements in a 350
straight-line form do not influence data variation. Small angles indicate a positive correlation, 351
large curves indicate negative correlations, and 90° curves show no correlation between the 352
two characteristics. 353
354
355
356
357
Farmers' Income After the Earthquake and Liquefaction 358
The results of the partial analysis of the effect of the independent variable on the income of 359
farmers after the earthquake and liquefaction showed that the means of production (X2) and 360
government assistance (X3) much determine the income of farmers when they face difficulties 361
in maintaining their life with their family as presented in Figure Table 6 and Figure 3. 362
363
Table 6. The Influence of Factors for Agricultural Production Facilities and Government 364
Assistance Post-Earthquake and Liquefaction Farmers' Income in Jono Oge and Toaya 365
Variable Indikator
Estimate Stand. of Error Probability
Intercept -8.004e-01 9.173e-01 0.3892
Extension (X1) 5.225e-01 2.781e-01 0.0691
Agric. Prod. Facilities (X2) 6.517e-16 3.872e-01 0.0150*
Gov. Aid (X3) 2.416e-01 1.217e-01 0.0354*
Covid-19 Information (X4) -1.026e-01 1.471e-01 0.0701
366
367
Table 6 shows the probability value (p) 0.0150 <∝ 0.05 for agricultural production facilities 368
and (p) 0.0354 <∝ 0.05 for government assistance. Figure 4 explains that the histogram, which 369
is below the ∝ 0.05 line, shows a significant effect on the probability of farmers' income after 370
the earthquake and liquefaction. 371
372
373
0.0691
0.015
0.0354
0.0701
0.05 0.05 0.05 0.05
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Extension Agricultural
Production
Facilities
Government
Aid
Covid-19
Information
Independent Variable Probability (p) (0.05)
Figure 4. The Value of the Independent Variable Probability of the Significance of Income 374
Post-Earthquake Farmers and Liquefaction 375
After the Earthquake and Liquefaction, various efforts have been made by the local government 376
in collaboration with NGOs and support from the central government. Still, the schemes that 377
most determine farmers' activities, which can significantly affect their income, are two factors, 378
namely the provision of production facilities and cash assistance milking (Table 6). In contrast, 379
counselling and information on the massive Covid-19 pandemic did not affect farmers' income, 380
as seen at the p-value (0.0691> 0.05) for the extension factor and p (0.0701> 0.05) as presented 381
in Figure 4. Earthquake and liquefaction as a natural event have reduced all the potential in 382
society, especially farmers who rely on their livelihoods from farming activities (Kamyab et 383
al., 2019; Naeem and Begum, 2020; Basir-Cyio et al., 2021). In an uncertain situation, 384
government assistance in the form of food, shelter, and clothing accompanied by strategic 385
policies can accelerate recovering the socio-economic conditions of communities affected by 386
earthquakes and Liquefaction (Pathak and Ahmad, 2018; Su and Le Dé, 2020). Every human 387
being has the resistance to survive, but without various parties' help, the recovery process will 388
be slow and take a long time (Nawari and Ravindran, 2019; Nguyen-Trung et al., 2020). 389
Government assistance in the form of cash and provision of production facilities is the right 390
step in restoring traumatic conditions experienced by farmers and farmers’ families. Reducing 391
the feeling of trauma and building a spirit to rise again into new energy is needed in resuming 392
life slowly and gradually (Bountress et al., 2020; Lai et al., 2018). 393
Farmers Income During Covid -19 Pandemic 394
The difficult conditions faced by farmers during the Covid-19 Pandemic were related 395
to assess ability and the availability of food needs and agricultural production facilities. 396
Farmers' income experiences high fluctuation accompanied by low purchasing power. The 397
results of the simultaneous and partial analysis of the four independent variable factors showed 398
that factor (X2) the availability of agricultural production facilities had a significant effect on 399
farmers' income during the Covid-19 Pandemic with substantial value (0.000) <of ∝ (0.05), as 400
presented in Table 7. 401
402
Table 7. The results of the analysis of the influence of the independent variables on farmers' 403
income during the Covod-19 Pandemic 404
Location Coefficients
Model B SE Beta T Sig. R2
Jono Oge, Sigi
Regency
(Constant) 0,105 0,090 1,164 0,252 0,905
Agric. Prod.
Facilities 0,947 0,068 0,951 13,856* 0,000
Government Aid 3,145E-015 0,110 0,000 0,000 1,000
Covid-19
Information -2,334E-015 0,107 0,000 0,000 1,000
Toaya, Donggala
Regency
Agric. Prod.
Facilities 0 ,579 0,151 0,874 3,830* 0 ,000 0,64
Government Aid 1,690E-015 0,243 0,000 0 ,000 1,000
Covid-19
Information -2,319E-0.15 0,236 0,000 0 ,000 1,000
405
In Table 7, it can be seen that the results of the correlation test between the independent 406
variables and farmers' income during the Covid-19 pandemic show a value (R2) of 0.95 or 407
95%, and 64% of farmers' income is determined by the availability of production facilities in 408
Jono Oge and Toaya, respectively. In maintaining agricultural businesses' sustainability in 409
uncertain situations, agricultural production facilities are crucial to support the farm production 410
process (Baumert et al., 2019; M. Li et al., 2020). Production facilities such as fertilizers, 411
pesticides, and superior seeds must be imported from outside Sulawesi Island, so they are very 412
dependent on transportation. Restrictions on the sea and air transportation by the government 413
to prevent the transmission of the Corona Virus are the leading cause of the scarcity of 414
production facilities on the market, making it difficult for local governments to help affected 415
farmers. During the Covid-19 pandemic period or one year after the Earthquake and 416
Liquefaction, government assistance in the form of cash had a significant effect on farmers' 417
income (Figure 5) with a probability value (p) 0.0282 <∝ (0.05). 418
419
Figure 5. Histogram of Probability of Independent Variables on Farmers' Income in Period 420
The Covid-19 pandemic in Jono Oge (Sigi Regency) and Toaya (Donggala Regency) 421
422
One year of the recovery period after the earthquake and liquefaction, farmers are 423
slowly fixing their lives. Still, living in accordwith the Health protocol has a destructive impact 424
on farmers livelihood, so cash assistance from the government is urgently needed. The Covid-425
19 pandemic period has been a factor in the decline in people's purchasing power in almost all 426
corners of the world (Barichello, 2020; Naryono, 2020). 427
428
CONCLUSION 429
The combination of M3P3 treatment gave the highest effect for all observations, namely 430
(i) pH-H2O (6.13), (ii) P-total (52.28 mg/100g), (iii) P-available (28.62 ppm), (iv) plant dry 431
weight (713.75 g/plot), (v) P-uptake (0.58 g/plant), and (vi) Corncob (6.2 t/ha). The results of 432
the PCA (Principal Component Analysis) analysis showed the highest and greatest factors that 433
affected variation, X2 (PC-1) 75.55%; X3 (PC-2) 18.22%; and X4 (PC-3) 3.94%, while X1 (PC-434
4) did not cause variation (2.28%). The results of the probability analysis show that the income 435
of farmers a year after the earthquake is influenced by the factor of production facilities 436
availability (X2) p (0.015) <∝ (0.05) and government assistance (X3) p (0.035) <∝ (0.05). At 437
the time of the Covid-19 pandemic, government assistance (X3) p (0.028) <∝ (0.05) had the 438
most significant effect on farmers' income, with a Correlation Value for each location (i) Jono 439
Oge (R) 0.91 and Toaya (R) 0.64. This shows that the main component factor has a higher level 440
0.9164
1
0.02820.1038
0.05 0.05 0.05 0.050
0.2
0.4
0.6
0.8
1
1.2
Extension Agricultural
Production Facilities
Goverment Aid Covid-19 Information
Pro
bab
ilit
y V
alu
e
Variable Independent Probability alpha (0,05)
of closeness in Jono Oge (0.91) as agricultural land is more affected by liquefaction compared 441
to land in Toaya (0.64), which is less affected by the earthquake without coinciding 442
liquefaction. 443
444
445
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Figures
Figure 1
Research locations in Jono Oge, Sigi and Toaya Regencies Donggala, Central Sulawesi Province,Indonesia
Figure 2
Plotting of the dominant main components, namely PC-1 (X2) Agricultural Production Facilities, PC-2 (X3)Government Assistance, and Supporting Components, namely PC-3 (X4) Covid-19 Information toFarmers.
Figure 3
The PC sequence diagnostic plot is most vital in capturing the diversity of factors.
Figure 4
The Value of the Independent Variable Probability of the Signi�cance of Income Post-Earthquake Farmersand Liquefaction
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