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

REFERENCES 446

447

Ahsanuzzaman, Islam, M.Q., 2020. Children’s vulnerability to natural disasters: Evidence 448

from natural experiments in Bangladesh. World Development Perspectives 19, 449

100228. https://doi.org/10.1016/j.wdp.2020.100228 450

Bakar, A., Kuncoroaji, T.I., Lee, A., Lee, S.-C., Ningrum, V., 2020. Epidemiological 451

Characteristics of Early COVID-19 Case Outbreaks in Indonesia (preprint). 452

MEDICINE & PHARMACOLOGY. 453

https://doi.org/10.20944/preprints202007.0367.v1 454

Balezentis, T., Ribasauskiene, E., Morkunas, M., Volkov, A., Streimikiene, D., Toma, P., 455

2020. Young farmers’ support under the Common Agricultural Policy and 456

sustainability of rural regions: Evidence from Lithuania. Land Use Policy 94, 104542. 457

https://doi.org/10.1016/j.landusepol.2020.104542 458

Barichello, R., 2020. The COVID‐19 pandemic: Anticipating its effects on Canada’s 459

agricultural trade. Canadian Journal of Agricultural Economics/Revue canadienne 460

d’agroeconomie 68, 219–224. https://doi.org/10.1111/cjag.12244 461

Basir-Cyio, M., Liwesigi, F., Bakri, T., Fardhal, M., Laihi, M.A., Dwitama, M.A., 2020. 462

Implementation of Communal Management of Covid-19 Socio-Information to 463

Agriculture Workers on Land Affected by Earthquake & Liquefaction Disasters. 464

Talent Development 15. 465

Basir-Cyio, M., Mahfudz, Isrun, Zeffitni, 2021. The impact of liquefaction disaster on 466

farming systems at agriculture land based on technical and psychosocial perspectives. 467

PLoS ONE 16, e0245591. https://doi.org/10.1371/journal.pone.0245591 468

Baumert, S., Fisher, J., Ryan, C., Woollen, E., Vollmer, F., Artur, L., Zorrilla-Miras, P., 469

Mahamane, M., 2019. Forgone opportunities of large-scale agricultural investment: A 470

comparison of three models of soya production in Central Mozambique. World 471

Development Perspectives 16, 100145. https://doi.org/10.1016/j.wdp.2019.100145 472

Bountress, K.E., Gilmore, A.K., Metzger, I.W., Aggen, S.H., Tomko, R.L., Danielson, C.K., 473

Williamson, V., Vladmirov, V., Ruggiero, K., Amstadter, A.B., 2020. Impact of 474

disaster exposure severity: Cascading effects across parental distress, adolescent 475

PTSD symptoms, as well as parent-child conflict and communication. Social Science 476

& Medicine 264, 113293. https://doi.org/10.1016/j.socscimed.2020.113293 477

Boyraz, G., Legros, D.N., Tigershtrom, A., 2020. COVID-19 and traumatic stress: The role 478

of perceived vulnerability, COVID-19-related worries, and social isolation. Journal of 479

Anxiety Disorders 76, 102307. https://doi.org/10.1016/j.janxdis.2020.102307 480

Cervantes‐Arslanian, A., Lau, K.H.V., Anand, P., Shulman, J.G., Ford, T., Takahashi, C., 481

Daneshmand, A., Nguyen, T.N., Suchdev, K., Abdennadher, M., Saxena, A., Macone, 482

A., Perloff, M.D., Zhu, S., Greer, D.M., 2020. Rapid Dissemination of Protocols for 483

Managing Neurology Inpatients with COVID ‐19. Ann Neurol 88, 211–214. 484

https://doi.org/10.1002/ana.25774 485

Chamberlain, W., Anseeuw, W., 2019. Inclusive businesses in agriculture: Defining the 486

concept and its complex and evolving partnership structures in the field. Land Use 487

Policy 83, 308–322. https://doi.org/10.1016/j.landusepol.2019.02.008 488

Chapagain, T., Raizada, M.N., 2017. Impacts of natural disasters on smallholder farmers: 489

gaps and recommendations. Agric & Food Secur 6, 39. 490

https://doi.org/10.1186/s40066-017-0116-6 491

Dhulipala, S., Patil, G.R., 2020. Freight production of agricultural commodities in India using 492

multiple linear regression and generalized additive modelling. Transport Policy 97, 493

245–258. https://doi.org/10.1016/j.tranpol.2020.06.012 494

Dupas, P., Robinson, J., Saavedra, S., 2020. The daily grind: Cash needs and labor supply. 495

Journal of Economic Behavior & Organization 177, 399–414. 496

https://doi.org/10.1016/j.jebo.2020.06.017 497

Gilbert, N., Mewis, R.E., Sutcliffe, O.B., 2020. Classification of fentanyl analogues through 498

principal component analysis (PCA) and hierarchical clustering of GC–MS data. 499

Forensic Chemistry 21, 100287. https://doi.org/10.1016/j.forc.2020.100287 500

Heo, W., Lee, J.M., Park, N., 2020. Financial-related psychological factors affect life 501

satisfaction of farmers. Journal of Rural Studies S0743016720303867. 502

https://doi.org/10.1016/j.jrurstud.2020.08.053 503

Huang, N., Chang, Y., Chou, C., 2020. Effects of creative thinking, psychomotor skills, and 504

creative self-efficacy on engineering design creativity. Thinking Skills and Creativity 505

37, 100695. https://doi.org/10.1016/j.tsc.2020.100695 506

Islam, M., Jamil, M., Chowdhury, M., Kabir, M., Rimi, R., 2020. Impacts of cyclone and 507

flood on crop and fish production in disaster prone coastal Bhola district of 508

Bangladesh. Int. J. Agril. Res. Innov. & Tech. 10, 40–55. 509

https://doi.org/10.3329/ijarit.v10i1.48093 510

Kamyab, S., Nematollahi, M., Yousefpour, F., Karimi, K., 2019. Investigating the 511

effectiveness of outfitting nuclear power plant with automatic seismic trip system 512

based on the early detection of earthquake. Soil Dynamics and Earthquake 513

Engineering 123, 162–172. https://doi.org/10.1016/j.soildyn.2019.04.029 514

Klomp, J., Hoogezand, B., 2018. Natural disasters and agricultural protection: A panel data 515

analysis. World Development 104, 404–417. 516

https://doi.org/10.1016/j.worlddev.2017.11.013 517

Lai, B.S., Osborne, M.C., Lee, N., Self-Brown, S., Esnard, A.-M., Kelley, M.L., 2018. 518

Trauma-informed schools: Child disaster exposure, community violence and somatic 519

symptoms. Journal of Affective Disorders 238, 586–592. 520

https://doi.org/10.1016/j.jad.2018.05.062 521

Lankoski, J., Thiem, A., 2020. Linkages between agricultural policies, productivity and 522

environmental sustainability. Ecological Economics 178, 106809. 523

https://doi.org/10.1016/j.ecolecon.2020.106809 524

Li, J., Zhang, Xiaochen, Luo, J., Lindsey, S., Zhou, F., Xie, H., Li, Y., Zhu, P., Wang, L., 525

Shi, Y., He, H., Zhang, Xudong, 2020. Differential accumulation of microbial 526

necromass and plant lignin in synthetic versus organic fertilizer-amended soil. Soil 527

Biology and Biochemistry 149, 107967. https://doi.org/10.1016/j.soilbio.2020.107967 528

Li, M., Wang, J., Zhao, P., Chen, K., Wu, L., 2020. Factors affecting the willingness of 529

agricultural green production from the perspective of farmers’ perceptions. Science of 530

The Total Environment 738, 140289. https://doi.org/10.1016/j.scitotenv.2020.140289 531

Liesivaara, P., Myyrä, S., 2017. The demand for public–private crop insurance and 532

government disaster relief. Journal of Policy Modeling 39, 19–34. 533

https://doi.org/10.1016/j.jpolmod.2016.12.001 534

List, J., 2020. Editorial: Volume 61: Intellectual Property information in the time of corona 535

virus. World Patent Information 61, 101971. 536

https://doi.org/10.1016/j.wpi.2020.101971 537

Liu, T., Gong, D., Xiao, J., Hu, J., He, G., Rong, Z., Ma, W., 2020. Cluster infections play 538

important roles in the rapid evolution of COVID-19 transmission: A systematic 539

review. International Journal of Infectious Diseases 99, 374–380. 540

https://doi.org/10.1016/j.ijid.2020.07.073 541

Luciano, E.M., 2020. Information management hits and misses in the COVID19 emergency 542

in Brazil. International Journal of Information Management 102194. 543

https://doi.org/10.1016/j.ijinfomgt.2020.102194 544

McCullagh, P., Nelder, J.A., 1989. Generalized Linear Models, 2nd Edition. ed. Chapman 545

and Hall, London. 546

Mosquera-Losada, M.R., Amador-García, A., Rigueiro-Rodríguez, A., Ferreiro-Domínguez, 547

N., 2019. Circular economy: Using lime stabilized bio-waste based fertilisers to 548

improve soil fertility in acidic grasslands. CATENA 179, 119–128. 549

https://doi.org/10.1016/j.catena.2019.04.008 550

Naeem, Z., Begum, S., 2020. Impact of seasonal variations on soil electrical conductivity as 551

an earthquake precursor along the Margalla Fault Line, Islamabad. Soil Dynamics and 552

Earthquake Engineering 137, 106233. https://doi.org/10.1016/j.soildyn.2020.106233 553

Nakagawa, K., Yu, Z.-Q., Berndtsson, R., Hosono, T., 2020. Temporal characteristics of 554

groundwater chemistry affected by the 2016 Kumamoto earthquake using self-555

organizing maps. Journal of Hydrology 582, 124519. 556

https://doi.org/10.1016/j.jhydrol.2019.124519 557

Naryono, endang, 2020. IMPACT OF NATIONAL DISASTER COVID-19, INDONESIA 558

TOWARDS ECONOMIC RECESSION (preprint). Open Science Framework. 559

https://doi.org/10.31219/osf.io/5cj3d 560

Nawari, N.O., Ravindran, S., 2019. Blockchain and Building Information Modeling (BIM): 561

Review and Applications in Post-Disaster Recovery. Buildings 9, 149. 562

https://doi.org/10.3390/buildings9060149 563

Nguyen-Trung, K., Forbes-Mewett, H., Arunachalam, D., 2020. Social support from bonding 564

and bridging relationships in disaster recovery: Findings from a slow-onset disaster. 565

International Journal of Disaster Risk Reduction 46, 101501. 566

https://doi.org/10.1016/j.ijdrr.2020.101501 567

Nurhadi, Suparmini, Sutrisnowati, S.A., Septiana, M.E., Praptiwi, N.Y., 2019. Farmers 568

Household Multidimentional Level and Its Effect on Participation in Agricultural 569

Land Conservation in Disaster Residents. IOP Conf. Ser.: Earth Environ. Sci. 271, 570

012007. https://doi.org/10.1088/1755-1315/271/1/012007 571

Nwanaji-Enwerem, J.C., Allen, J.G., Beamer, P.I., 2020. Another invisible enemy indoors: 572

COVID-19, human health, the home, and United States indoor air policy. J Expo Sci 573

Environ Epidemiol 30, 773–775. https://doi.org/10.1038/s41370-020-0247-x 574

Olsen, R.K., Pickard, V., Westlund, O., 2020. Communal News Work: COVID-19 Calls for 575

Collective Funding of Journalism. Digital Journalism 8, 673–680. 576

https://doi.org/10.1080/21670811.2020.1763186 577

Pathak, S., Ahmad, M.M., 2018. Role of government in flood disaster recovery for SMEs in 578

Pathumthani province, Thailand. Nat Hazards 93, 957–966. 579

https://doi.org/10.1007/s11069-018-3335-7 580

Peng, L., Tan, J., Deng, W., Liu, Y., 2020. Farmers’ participation in community-based 581

disaster management: The role of trust, place attachment and self-efficacy. 582

International Journal of Disaster Risk Reduction 51, 101895. 583

https://doi.org/10.1016/j.ijdrr.2020.101895 584

Rajindra, R., Anggareni, R.N., Yani, A., Akkas, N., 2020. Economic Recovery in the 585

Aftermath of Earthquake, Tsunami, and Liquefaction disaster during the COVID 19 586

Pandemic Situation. Talent Development 8. 587

Rollett, R., Collins, M., Tamimy, M.S., Perks, A.G.B., Henley, M., Ashford, R.U., 2020. 588

COVID-19 and the Tsunami of Information. Journal of Plastic, Reconstructive & 589

Aesthetic Surgery S1748681520304617. https://doi.org/10.1016/j.bjps.2020.08.112 590

Singh, C., Tiwari, S., Gupta, V.K., Singh, J.S., 2018. The effect of rice husk biochar on soil 591

nutrient status, microbial biomass and paddy productivity of nutrient poor agriculture 592

soils. CATENA 171, 485–493. https://doi.org/10.1016/j.catena.2018.07.042 593

Song, H., Wang, J., Zhang, K., Zhang, M., Hui, R., Sui, T., Yang, L., Du, W., Dong, Z., 594

2020. A 4-year field measurement of N2O emissions from a maize-wheat rotation 595

system as influenced by partial organic substitution for synthetic fertilizer. Journal of 596

Environmental Management 263, 110384. 597

https://doi.org/10.1016/j.jenvman.2020.110384 598

Soron, T.R., 2019. Future strategies to combat the hidden burden of suicide among farmers in 599

South East Asia. Asian Journal of Psychiatry 42, 18. 600

https://doi.org/10.1016/j.ajp.2019.03.016 601

Su, Y., Le Dé, L., 2020. Whose views matter in post-disaster recovery? A case study of 602

“build back better” in Tacloban City after Typhoon Haiyan. International Journal of 603

Disaster Risk Reduction 51, 101786. https://doi.org/10.1016/j.ijdrr.2020.101786 604

Su, Y.-L., Wang, Y.-F., Ow, D.W., 2020. Increasing effectiveness of urban rooftop farming 605

through reflector-assisted double-layer hydroponic production. Urban Forestry & 606

Urban Greening 54, 126766. https://doi.org/10.1016/j.ufug.2020.126766 607

Tosepu, R., Gunawan, J., Effendy, D.S., Ahmad, L.O.A.I., Lestari, H., Bahar, H., Asfian, P., 608

2020. Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. 609

Science of The Total Environment 725, 138436. 610

https://doi.org/10.1016/j.scitotenv.2020.138436 611

Trnka, R., Lorencova, R., 2020. Fear, anger, and media-induced trauma during the outbreak 612

of COVID-19 in the Czech Republic. Psychological Trauma: Theory, Research, 613

Practice, and Policy 12, 546–549. https://doi.org/10.1037/tra0000675 614

Ulhaq, Z.S., Soraya, G.V., 2020. The prevalence of ophthalmic manifestations in COVID-19 615

and the diagnostic value of ocular tissue/fluid. Graefes Arch Clin Exp Ophthalmol 616

258, 1351–1352. https://doi.org/10.1007/s00417-020-04695-8 617

Wammes, J.D., Kolk, D., van den Besselaar, J.H., MacNeil- Vroomen, J.L., Buurman- van 618

Es, B.M., van Rijn, M., 2020. Evaluating perspectives of relatives of nursing home 619

residents on the nursing home visiting restrictions during the COVID-19 crisis: a 620

Dutch cross-sectional survey study. Journal of the American Medical Directors 621

Association S1525861020308318. https://doi.org/10.1016/j.jamda.2020.09.031 622

Wu, B., 2020. How small farmers enter the big market? A case study of agricultural 623

cooperatives in China, in: Waking the Asian Pacific Co-Operative Potential. Elsevier, 624

pp. 113–124. https://doi.org/10.1016/B978-0-12-816666-6.00010-0 625

Xie, S., Yang, F., Feng, H., Yu, Z., Liu, C., Wei, C., Liang, T., 2020. Organic fertilizer 626

reduced carbon and nitrogen in runoff and buffered soil acidification in tea 627

plantations: evidence in nutrient contents and isotope fractionations. Science of The 628

Total Environment 143059. https://doi.org/10.1016/j.scitotenv.2020.143059 629

Xu, Y., Ding, X., Lal, R., Gao, X., Li, S., Sun, L., Wang, Y., Li, M., Bai, S., Wang, J., 2020. 630

Effect of soil fertility on the allocation of nitrogen derived from different maize 631

residue parts in the soil-plant system. Geoderma 379, 114632. 632

https://doi.org/10.1016/j.geoderma.2020.114632 633

Yanti, B., Wahyudi, E., Wahiduddin, W., Novika, R.G.H., Arina, Y.M.D., Martani, N.S., 634

Nawan, N., 2020. COMMUNITY KNOWLEDGE, ATTITUDES, AND BEHAVIOR 635

TOWARDS SOCIAL DISTANCING POLICY AS PREVENTION 636

TRANSMISSION OF COVID-19 IN INDONESIA. JAKI 8, 4. 637

https://doi.org/10.20473/jaki.v8i2.2020.4-14 638

Zhong, Y., Liu, W., Lee, T.-Y., Zhao, H., Ji, J., 2020. Risk perception, knowledge, 639

information sources and emotional states among COVID-19 patients in Wuhan, 640

China. Nursing Outlook S0029655420306199. 641

https://doi.org/10.1016/j.outlook.2020.08.005 642

643

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

Figure 5

Histogram of Probability of Independent Variables on Farmers' Income in Period The Covid-19 pandemicin Jono Oge (Sigi Regency) and Toaya (Donggala Regency)