Social Design Engineering Series

SDES-2024-2

Farm sizes and adaptation responses to climate change in
agriculture: A reflection of Tajikistan’s farming culture
and history

Husniddin Sharofiddinov
School of Economics and Management, Kochi University of Technology
Scientific research Institute for hydraulic engineering and melioration “TajikNIIGiM”, Republic of
Tajikistan

Moinul Islam
School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology

Koji Kotani
School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology
Urban Institute, Kyushu University
College of Business, Rikkyo University

May 15, 2024
School of Economics and Management
Research Institute for Future Design
Kochi University of Technology
KUT-EMS working papers are preliminary research documents published by the School of Economics and Management jointly with the ResearchInstitute for Future Design at Kochi University of Technology. To facilitate prompt distribution, they have not been formally reviewed and edited.
They are circulated in order to stimulate discussion and critical comment and may be revised. The views and interpretations expressed in these
papers are those of the author(s). It is expected that most working papers will be published in some other form.

Farm sizes and adaptation responses to climate change
in agriculture: A reflection of Tajikistan’s farming
culture and history
Husniddin Sharofiddinov*,† Moinul Islam*,‡ Koji Kotani*,‡,§,¶,||
May 15, 2024

Abstract
Climate change is a global concern, having a negative impact on agriculture, for food security and sustainability. Farmers’ adaptations are known to be key drivers for the resolutions.
However, little is established about relationships between farmers’ characteristics and adaptation responses to climate change under irrigated agriculture. We investigate how farm sizes
influence the adaptations in consideration to irrigation-related, cognitive and socioeconomic
factors reflecting farming culture and history, hypothesizing that large-size farms adapt to climate change as compared to small-size ones in Tajikistan, where collective farming, “Kolkhoz
and Sovkhoz,” had been practiced. The data were collected through a questionnaire survey with
800 farmers on their adaptations, farm sizes, climatic perceptions, irrigation water availability
and socioeconomic factors. We conduct statistical analyses utilizing the index to characterize
farmers’ adaptation responses. The results indicate an importance of farm sizes on adaptations,demonstrating that small-size farms adapt less than large-size farms, but increases their adaptations when they have good climatic perceptions and irrigation water availability. Overall, thisresearch confirms an advantage of large-size farms for adaptations based on Tajikistan farming
culture and history. Thus, the ongoing land-fragmentation policy should be reconsidered forpossible losses in adaptations, as it has been drastically increasing the number of small-size
farms. Otherwise, it is essential to support the small-size farms for acquiring good perceptions
and enough water.

Key Words: Farm sizes; adaptation responses; farming culture; history; Tajikistan* School of Economics and Management, Kochi University of Technology†

Scientific research Institute for hydraulic engineering and melioration “TajikNIIGiM”, Republic of Tajikistan
Research Institute for Future Design, Kochi University of Technology§
Urban Institute, Kyushu University¶
College of Business, Rikkyo University||
Corresponding author, E-mail: kojikotani757@gmail.com‡

1

Contents
1

Introduction

3

2

Farming culture and history

5

3

Data and methodology
9
3.1 Study areas and data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Key variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Conceptual framework and data analysis . . . . . . . . . . . . . . . . . . . . . . . 15

4

Results

18

5

Conclusion

29

Nomenclature
AdaptInd Adaptation index
AdapNum Adaptation number
CPInd Climatic perceptions index
IWA Irrigation water availability

2

1

1

Introduction

2

Climate change has become a growing global concern and posed a negative impact on agri-

3

culture, for food security and sustainability (Rosegrant and Cline, 2003, Burkel and Lobell, 2010,

4

Hanjra and Qureshi, 2010, Ziervogel and Ericksen, 2010, Sasson, 2012, Mahato, 2014, Nelson and5van der Mensbrugghe, 2014, Campbell et al., 2016, Orchard et al., 2020, Din et al., 2022). It is6established that climate change cannot be resolved only by mitigation (Jensen and Traeger, 2014,

7

Fujimori et al., 2018). Rather, adaptations are keys to not only resolving climate change but also8

maintaining food security and sustainability under growing population with less resources, land

9

and water availability per capita (Linnenluecke et al., 2015, Rasul and Sharma, 2016). Particularly,

10

farmers are considered the main actors to initiate and take adaptation responses to climate change,

11

and their cognitive and socioeconomic factors are assumed to be the important determinants de-12pending on culture and history in each region or country (Dixon et al., 2014, Huttunen et al., 2015,13

Dang et al., 2019, Arnalte-Mur et al., 2020, Ardakani et al., 2020). Given this state of affairs, we

14seek to address how farmers take adaptations in relation to cognitive and socioeconomic factors

15that reflect farming culture and history.

16

In the last few decades, the impact of climate change on agricultural productivity has been

17

studied, using agronomic models and statistical approaches (Moore et al., 2017, Li et al., 2023b).

18

For instance, the statistical approaches have been used to examine farmers’ adaptation responses in

19relation to cognitive and economic factors (Below et al., 2012, Esham and Garforth, 2013, Li et al.,202023a, Sandilya and Goswami, 2024, Sun et al., 2024). Several studies examine the relationship21

between climatic perceptions and the farmers’ adaptations (Deressa et al., 2011, Islam et al., 2016,22

Datta et al., 2022). Climatic perceptions and farm sizes are the key cognitive and socioeconomic

23

determinants for farmers’ adaptations to climate change (Koirala et al., 2022). Several empiri-

24

cal studies have explored the adaptations in relation to farm sizes, documenting both the negative25

and positive results (Khan et al., 2020). The possible reasons for negative or positive results on

26

adaptations are claimed to be specific characteristics of the study areas, such as geographic loca-

27

tions, cognitive and socioeconomic factors (Bobojonov and Aw-Hassan, 2014, Amare and Simane,3282017).

29

A group of studies have shown a negative relation between farm sizes and adaptation responses

30

to climate change (Acquah, 2011, Deressa et al., 2011, Tesfaye and Seifu, 2016, Maya et al., 2019).

31

Uddin et al. (2014) investigate the farmers’ adaptation practices to environmental destruction and32

climate change by collecting a questionnaire data with 100 farmers in Bangladesh, documenting

33

that the probability of adaptation responses decreases in large-size farms due to shortage of in-34

vestments. Kabubo-Mariara and Mulwa (2019) study the factors affecting adaptations to climate

35

change and variability by taking 658 households in Kenya as a sample, finding that an increase36

in farm sizes have an adverse relationship with maize grain production for both adapters and non-37

adapters. Koirala et al. (2022) examine adaptation responses to climate change in relation to farm38

sizes along with cognitive and sociodemographic factors by taking a sample of 1000 farmers in

39

Nepal. The results indicate that small-size farms adapt with their good climatic perceptions and40

networking with other farmers as compared to large-size farms. Overall, these studies present that41

small-size farms tend to respond to climate change with less required inputs and investments as

42compared to large-size farms.

43

Another group of studies shows some positive association between farm sizes and farmers’

44

adaptation responses to climate change (Nabikolo et al., 2012, Abid et al., 2016, Vinaya et al., 2017,

45

Bakhsh and Kamran, 2019, Thinda et al., 2020). Several studies indicate that farm sizes, education

46

level, access to climate information, market, subsidies and credits have positive relationship with47

farmers’ adaptations to climate change (Alauddin and Sarker, 2014, Belay et al., 2017, Fadina and48

Barjolle, 2018, Marie et al., 2020, Ojo and Baiyegunhi, 2020). Ali and Erenstein (2017) investigate

49

determinants of farmers’ decisions on adaptation practices using a questionnaire survey from 950

50

farmers under both irrigated and rain-fed agriculture in Pakistan, presenting that the number of51

adaptations tends to increase in farm sizes. Trinh et al. (2018) examine some key variables that shall52

be considered to impact farmers’ choices for adaptations in agricultural productions, taking 400

53

farmers from Ky Son commune in Vietnam, indicating that farmers tend to change crop varieties,

54

switch to new cultivar types and adjust the planting time when their farm size increases. Jha

4

55

and Gupta (2021) study farmers’ climatic perceptions and socioeconomic factors in relation to56

adaptation strategies under rain-fed agriculture, collecting 700 farmers’ data in seven districts of

57

northern India. They find that large-size farms tend to implement most of the adaptation strategies,

58

such as crop diversification, irrigation and soil management. Overall, the literature document that59

large-size farms tend to respond to climate change, being able to invest on technologies, machinery,60

infrastructures and other inputs, as compared to small-size farms.

61

Agricultural sector have been reported to be one of the most vulnerable sectors against climate

62

change. Thus, it is important to understand how farmers are induced to take adaptation responses

63

under modern agriculture and to take proper countermeasures for food security and sustainabil-

64

ity. While there are a series of articles to present some mixed results, little is established about

65

relationships between farmers’ characteristics and adaptation responses to climate change under66

irrigated agriculture. Given this paucity in the literature, we examine how farm sizes characterize

67

the adaptations in consideration to irrigation-related, cognitive and socioeconomic factors reflect-

68

ing farming culture and history. To this end, we conduct a questionnaire survey with 800 farmers in69

the two river basins of Tajikistan and collect data on their adaptation practices, farm sizes, climatic

70

perceptions (CPInd), irrigation water availability (IWA), cognitive and socioeconomic variables.

71

We conduct econometric analyses, employing an index which reflects the coverage areas by farm-72

ers’ adaptation responses. This research hypothesizes that large-size farms adapt to climate change73

as compared to small-size ones under irrigated agriculture in Tajikistan, “Kolkhoz and Sovkhoz,”

74

had been practiced. Answering the question and hypothesis shall be useful for guiding the ongoing75

debate regarding land-fragmentation and adaptation policies in not only Tajikistan but also other76

nations facing similar problems towards sustainable development goals (SDGs).

77

2

Farming culture and history

78

Tajikistan is a landlocked country in Central Asia and 93 % of its territory is covered with79

mountains. While the agriculture is engaged in non-mountainous areas, i.e., the rest of 7 %, Tajik-5

80

istan is still considered an agricultural country with a population of more than 10 million. Agricul-

81

ture is the backbone of Tajikistan economy, contributing 24 % of the gross domestic product (GDP)82

and employing 46 % of the population (World Bank, 2021). The total potential area suitable for83

irrigation is estimated to be 1.57 million ha out of which 0.76 million ha is developed at present. In

84

Tajikistan, the availability of irrigated land per capita is 0.08 ha which is the lowest among Central

85

Asian countries (ALRI, 2020). Irrigated agriculture is critical for food security and sustainability,

86

providing more than 80 % of Tajikistan agricultural production (MEDT, 2013).

87

Development of large irrigation systems during the Soviet era had played an important role88

on improving food security and sustainability through transforming Tajikistan agriculture into be-89

ing one of the main sectors. The development of vast tracts in the northern and southern regions

90

demonstrated an economically profitable practice for the agricultural development in the past. The

91

implementation of state programs on construction of the large irrigation systems has began from

92

1930, including canals, pump stations, reservoirs and other supplementary infrastructures. In par-93

allel, a collectivization process took place on merging small and inefficient individual farms into94

a large state one, such as collective state farms (Kolkhoz and Sovkhoz) along with mechanization95

and processing. The state agricultural enterprises had achieved a steady increase in productivity,96

an increase in the gross harvest of agricultural products, having made not only a great contribution

97

to strengthening the food security of the states but also served as a model of cultures for collective

98

farms (Sulaymonshoev, 2011, Alimov, 2020).

99

For the development and management of the agricultural sector during the Soviet Union period,

100

some designated ministries had been working jointly with scientific-research institutes and experi-101

mental stations (Sotnikov, 1960). The collective and state farms had an adequate natural capital to102

accelerate their material-technical bases including mechanized parks, storage, product processing,103

cattle, poultry and seed breeding centers. In the 1970 - 1980, most of collective farms had good-

104

quality agricultural specialists with high education. Such specialists were economists, agronomists

105

and engineers and they were working on mechanization, irrigation and livestock management with

106

farmers. For instance, the irrigation specialists were responsible for controlling, monitoring and

6

107

supervising water allocations within the farms as well as for improving water use efficiency. The

108

agronomists were responsible for controlling, monitoring and supervising crop adjustments and ro-

109

tations, sowing and harvesting times, soil reclamation, breeding crop varieties, pests management110

as well as adaptations to weather changes. Overall, agricultural sector was managed in a complex

111

manner from planning to productions by the Soviet Union. However, the centralized approach had

112

the pros and cons depending on the temporal changes through implementations of various poli-

113

cies in some regions. The agricultural sector was known to have faced huge challenges, such as

114

inefficiencies, resource mismanagement and a lack of controls, and it became noncompetitive in a

115

globalized agricultural market after the Soviet Union (Rowe, 2010).

116

The collapse of the Soviet Union has been marked by political, institutional and sectoral

117

changes along with the degradation of government support during the transition from a centralized

118

to a market-based economy and have created significant challenges to agriculture in Tajikistan. In

119

the transitions, Tajikistan government had started decentralizing agricultural sector through adopt-

120

ing a series of state policies and programs for a land reform. The main goal of the land reform121

was to increase agricultural production, the degree of freedom for farmers, crop diversification and122

efficient use of natural resources (land and water). Following the decentralization, the collective

123

and state farms were reorganized to individual small-size farms. The number of farms had in-124

creased from 5713 in 1997 to 187 220 in 2020, while irrigated land has increased only a little from125

719 000 ha to 763 000 ha (SCLMG, 2020).

126

Figure 1a demonstrates an example where one specific area of 120 ha in the upstream region

127of Kofarnihon river basin consisted of 15 plots with an average size of 8 ha, and it was part of one

128

collective farm in 2005. Figure 1b show that the irrigation system including distribution canals

129

remains unchanged from 2005 to 2020, while the number of plots has increased up to 237 with

130

an average size of 0.5 ha in 2023. It implies that the area becomes highly fragmented through the131land reform with an increasing number of small-size farms. In other words, the average plot size132

has decreased approximately by 94 %. Consequently, several salient problems, such as the lack of133

appropriate infrastructure, problems related to water allocations, equipment and services, limited7

(a) Plot sizes in 2005(b) Plot sizes in 2023

Figure 1: Changing plot sizes from 2005 to 2023 and irrigation systems. Source: Google Earthimages for Kofarnihon river basin

8

134

information access and uncertainties, have persisted, and it is claimed that Tajikistan farms become

135less capable of adapting to external factors for food security and sustainability. To examine the136

claim, it is essential to study and understand the relationship between farm sizes and adaptations

137

to climate change for identifying a path to SDGs, considering an ongoing land-reform policy in138

Tajikistan.

139

3

Data and methodology

140

3.1

Study areas and data collection

141

In Tajikistan, the five river basin management organizations (Syrdarya, Zarafshon, Kofarnihon,

142

Vakhsh and Panj) were created for the proper planning and effective management of water re-143

sources. These river basin organizations are based on the hydrological and regional administrative

144

boundaries, considering hydrotechnical infrastructures, water use and economic circumstances.

145

Among them, Kofarnihon and Zarafshon river basins are chosen as our study areas. Kofarnihon

146

river basin is located in the central-southwest part, covering over 30 % of Tajikistan territory. The

147

climate is continental in the basin, being characterized by large fluctuations of daily and seasonal148

air temperatures as well as uneven distribution of precipitations throughout a year. The average

149

minimum air temperature is −12.1 °C and the absolute maximum reaches 41 °C in the upstream

150

regions, while the average minimum air temperature is 1.8 °C to 1.9 °C and the absolute maximum151

temperature reaches 47 °C in the downstream regions. The average annual precipitations range be-152

tween 252 mm and 1188 mm in the downstream and upstream regions, respectively (Gulakhmadov

153

et al., 2020). Zarafshon river basin is located in the central-western part of Tajikistan, covering

154

approximately 9 % of Tajikistan territory. The average minimum air temperature is −12.1 °C and155

the absolute maximum reaches 35 °C in the upstream regions, while the average minimum air

156

temperature is −1 °C and the absolute maximum temperature reaches 47 °C in the downstream re-157

gions. The average annual precipitations range between 600 mm and 159 mm in the upstream and158

downstream regions, respectively (Normatov et al., 2023).
9

159

A stratified sampling method was utilized to conduct our questionnaire surveys in Kofarnihon

160

and Zarafshon river basins (see figure 2). We consider both upstream and downstream regions of161

Kofarnihon and Zarafshon river basins as the best possible proxy to approximate representative

162

agriculture with climatic zones from small-size to large-size farms in Tajikistan. A list of farmers

163

under irrigated areas was collected from each of the two river basins with support from the Ministry164

of Energy and Water Resources and Agency for Land Reclamation and Irrigation. We randomly165

selected 800 farmers with the strata of the upstream and downstream regions as well as the unequal

166

numbers of farms over the basins. As a consequence, 487 and 313 farmers were selected from the167

upstream and downstream regions, respectively, and a simple random sampling within each stratum

168

was applied to finalize the sample selection for our data collection. Because the two basins have169

unequal numbers of farms, we sought to reflect the difference through the above stratified sampling170

procedures. We conducted the orientation sessions over two days, training research assistants and171

conducting pretests of the questionnaires. The surveys had been conducted from May 15 to June

172

14, 2024 with local Tajikistan language. The 1st author administered the orientation sessions and173

surveys, organizing the research assistants for interviewing farms.

174

3.2

Key variables

175

The heads of farms (hereafter, farmers) were asked a series of questions related to their farm176

sizes, adaptations and the corresponding areas. A list of adaptations is prepared following Below177

et al. (2012) and Koirala et al. (2022). On top of it, we include some new adaptations, such as

178“night irrigation,” “drip irrigation,” and “film farming,” taking the farmers’ suggestions during the179

orientation sessions and field observations by the 1st author. In total, 34 adaptations are considered180

across water management, soil management and crop adjustment and diversification for the anal-

181

yses (see table 1 for the list). We also collect information related to cognitive and socioeconomic

182

factors of climatic perception index (CPInd), farming experience, information access, education,

183

irrigation water availability (IWA), equipment and services, distance to plots, income source, fam-184

ily size, gender and climatic zones (see table 2 for the details). We provide a list of adaptations to10

Figure 2: A map of study areas in Tajikistan

11

185

farmers, asking them to indicate their adaptation practices and its area coverage (Below et al., 2012,

186

Koirala et al., 2022). With the information, the key variables of adaptation number (AdaptNum)

187

and adaptation index (AdaptInd) in our statistical analyses are derived.

188

The AdaptNum for the ith farmer is calculated as follows:

AdaptNumi =

189

34
X

ahi

(1)

h=1

190

where subscript h denotes an index of adaptations from 1 to 34 and ahi is a dummy variable for191

adaptation h, taking 1 when the ith farmer adopts the adaptation, otherwise, 0. The AdaptInd for192

the ith farmer is calculated as follows:

193

AdaptIndi =34
X

ahi whi

(2)

h=1

194

Area coverage by ahi
where whi = Total cultivated∈ [0, 1]. Equations (1) and (2) imply that both AdaptNumi
area of the ith farmer

195

and AdaptIndi range between 0 and 34. The AdaptNumi is the summation of adaptations, while196

the AdaptIndi is a weighted average of adaptations by whi for the ith farmer.

197

The other key variables in our study shall be farm sizes, CPInd and IWA. We find that there is no198

official categorization of farm sizes in Tajikistan. Based on our field observations and discussions

199

with staff from the Ministry of Agriculture of the Republic of Tajikistan, we decide to categorize

200

farm sizes into three groups, creating two farm-size dummies along with one base group: (i) large-

201

size farms (farm sizes > 3 ha) as a base group, medium-size farms (3 ha ≥ farm sizes ≥ 1 ha)

202

as the medium-size dummy and small-size farms (farm sizes < 1 ha) as the small-size dummy203(see table 2). Regarding CPInd, we ask farmers to answer 10 questions on their perceived changes

204

in summer and winter temperatures, precipitations, rainfall frequency and intensity, snowfall fre-205

quency and intensity, drought, cold waves and hot waves over the last 10 years, following the206

approach by Koirala et al. (2022) and the pretesting of our questionnaires. Farmers report their

207

perceptions on the changes in these climatic factors, and we assign 1 for the perceived “change,”

208

otherwise 0 (see table 2). Finally, we calculate CPInd to be the summation of their answers from12

Table 1: Percentages of farms that implement adaptation practices by their sizes

Percentages (%)
Adaptation practices

Agricultural water management
Night irrigation
Bucket irrigation
Construction of reservoirs and channels
Diversion ditches
HedgesUse of vertical wells and pumps
Drip Irrigation
Runoff harvesting
Destocking (drainage cleaning)
Aquaculture
Winter irrigation (Freezing)
Mulching
Soil management
Deep tillage (30-35sm)
Crop rotation
Extend farmland outside of the ward
Restoration of degraded lands
Crop adjustment and diversification
Cultivation of high-quality crops
Crop breeding
Planting short-term variety crops
Planting early crops
Planting high yielding varieties
Cover crops
Film farming
Adjustment to sowing date
Adoption of different varieties(drought/ pest/ heat stress resistant)
Cultivation of cold-resistant crops
Cereal crop production
Revegetation
Grow vegetables in off season
Mix cropping
Apply farm yard manure/ organic fertilizer
Apply inorganic fertilizer
Keep livestock
Keep bee farming

Large-size farm
(N = 261)

Medium-size farm
(N = 394)

Small-size farm
(N = 144)

Overall(N = 799)

81.99
0.77
0.00
4.60
16.86
4.98
1.15
0.77
15.71
3.45
58.62
1.92

67.51
1.78
0.51
6.09
4.06
2.54
0.25
0.25
20.05
3.05
22.59
3.05

55.56
9.72
0.00
11.81
2.08
0.69
0.00
0.00
22.92
14.58
19.44
2.08

70.09
2.88
0.25
6.63
7.88
3.00
0.50
0.38
19.15
5.26
33.79
2.50

88.52
71.26
2.68
3.07

50.76
52.03
3.81
0.76

29.17
34.03
2.78
0.69

57.57
55.07
3.25
1.50

19.92
3.45
5.36
6.90
6.90
2.68
4.60
71.65

34.77
1.78
6.60
14.47
15.74
0.51
2.28
55.58

25.69
1.39
4.17
3.47
7.64
0.69
0.00
50.00

28.25
2.25
5.76
10.01
11.39
1.25
2.63
59.82

3.07
3.07
67.43
56.70
1.53
14.18
92.72
90.04
25.67
1.53

9.64
2.54
55.33
47.21
2.54
6.60
89.85
90.36
53.81
2.28

10.42
4.17
38.19
36.11
1.39
11.11
92.36
91.67
59.72
2.08

7.63
3.00
56.20
48.31
2.00
9.89
91.24
90.49
45.68
2.00

Notes: Large-size farm (farm sizes > 3 ha), medium-size farm (3 ha ≥ farm sizes ≥ 1 ha),small-size farm (farm sizes <1 ha).

13

Table 2: Definitions of the variables
Variables
Dependent variables
Adaptation number (AdaptNum)
Adaptation index (AdaptInd)

Independent variables
Farm-size variables
Large-size farm (base group)
Medium-size farm
Small-size farmCognitive variables
Climatic perception index (CPInd)

Farming experience

Information access
Education

Socioeconomic variables
Irrigation water availability (IWA)
Equipment and services
Distance to plots
Income source(base group = non agriculture)
Climatic zones (base group = upstream)
Family size
Gender (base group = male)

Definitions and descriptions
A total number of adaptations taken by the farmer.
The farmer’s aggregate index value is determinedby summing up all adaptations, each weighted according to its
proportion coverage relative to the farm sizes.

A dummy variable that takes value 1 if farm sizes is > 3 ha;otherwise, 0.
A dummy variable that takes value 1 if farm sizes is ≤ 3 ha & ≥ 1 ha;otherwise, 0.
A dummy variable that takes value 1 if farm sizes is < 1 ha;otherwise, 0.
Various changes in temperature, rainfall, snowfall, drought,
hot waves and cold waves perceived by farmer over the last 10 years,
rated on a scale of 0 to 10.
The level of agricultural experience for the farmer ranges between 1-5.
1 - less than five years; 2 - 5 to 10 years; 3 - 11 to 15 years;
4 - 16 to 20 years; 5 - more than 20 years
An aggregate number of access on agricultural information in local,
region, province and country levels that ranges between 0 - 32.
The level of schooling for the farmer ranges between 1-4,
1 - primary school; 2 - middle school; 3 - high school;
4 - university or above.
The level of water availability ranges between 1-5
1 - water does not reach and 5 - water abundant.
Summation of the number of farmers equipment types
and received agricultural services.
Distance in kilometers (km) from farmers home to their land.
A variable that takes value 1 if the farmers‘ main income
is from agriculture; otherwise, 0.
A dummy variable that takes value 1 if the farmer
is located in downstream; otherwise, 0.
The number of family members of the farmer.
A dummy variable that takes value 1 if the farmer is female;otherwise, 0.

14

Figure 3: A conceptual framework that describes the relationships between the variables (farmsizes, climatic perceptions, farming experience, information access, education, irrigation water
availability, equipment and services, income source, distance to plots, climatic zones, family size,
gender and interactions) and adaptation number (AdaptNum) or adaptation index (AdaptInd) by
βjK s for K = {AdaptNum, AdaptInd} and j = 0, 1, . . . , ℓ.

209

10 questions (Below et al., 2012, Koirala et al., 2022). The information on IWA is collected using

210

an ordinal five-point scale that represents 1 “water scarce,” ... and 5 “water abundant” (see table 2).

211

We use this way mainly due to the lack of other measurements for water availability at farm level212

in Tajikistan.

213

3.3

Conceptual framework and data analysis

214

Figure 3 introduces a conceptual framework for our empirical analyses on the basis of the215

observations for farmers in Tajikistan as well as some existing theories in adaptations. Tajikistan

216

farmers are known to have different objectives in their farming activities, being similar to other217

countries’ cases (Solano et al., 2001, Mandryk et al., 2014). Some farmers prioritize food security

218

along with their consumptions owning to various circumstances and situations, meaning that their
15

219

objectives are maximizing food production. On the other hand, other farmers focus on minimizing220

the risks, trying to avoid food production below certain thresholds as their 1st priority or stably

221

ensuring a certain level of cash incomes as their side businesses. Therefore, economic theories of222

profit maximization and cost minimization cannot be directly and uniformly applied to adaptations

223

in Tajikistan. Following Koirala et al. (2022), we refer to some theories in sociocognitive models,224

such as protection motivation theory and private proactive adaptation theory, for the conceptual

225

framework in this study. These approaches consider cognitive and socioeconomic factors to be

226

determinants of adaptation responses to external changes (Rogers, 1983, Rogers and Prentice-227

Dunn, 1997, Grothmann and Patt, 2005). Previous studies also underscore that such cognitive and228

socioeconomic factors are important for farmers’ adaptations in agriculture (Amare and Simane,229

2017, Belay et al., 2017, Ojo and Baiyegunhi, 2020).

230

The conceptual framework is developed to offer a comprehensive perspective for understanding231

how AdaptNum and AdaptInd are characterized by cognitive and socioeconomic variables along

232

with their interactions. To this end, we apply Poisson and median regressions to quantitatively

233

estimate parameters βjK s for j = 0, 1, . . . , ℓ and K = {AdaptNum, AdaptInd}, each of which

234

represents the relationship between the AdaptNum or AdaptInd as a dependent variables and the235

independent variables as specified in equations (3) and (4). For AdaptNum, we select a Poisson-236

regression approach, because it is a variable of nonnegative integers and there are only a limited

237

number of observations for each count. In other words, it is assumed that AdaptNum follows a238

Poisson distribution conditional on a vector of some independent variables, X, with the following

239

specification:240

Prob(AdaptNumi = h|X = xj ) = exp[− exp(xi β K′ )][exp(xi β K′ )]h /h!,241

where K = AdaptNum, h = 0, 1, . . . , 34 is the number of adaptations the ith farmer takes,

242

xi = (1, x1i , x2i , . . . , xℓi ) is a vector of ℓ + 1 independent variables consisting of intercept, farm-243

size, CPInd, IWA, cognitive, socioeconomic variables and the corresponding interaction terms,

16

(3)

244

respectively. Finally, β K = (β0K , β1K , . . . , βℓK ) is a vector of the coefficients associated with xi

245

to be estimated. The estimate for each coefficient is obtained through the quasi-maximum like-246

lihood estimation method for the Poisson regression based on equation (3) (Wooldridge, 2019).

247

We calculate a marginal effect of one independent variable on AdaptNum from each estimated248

coefficient, when the independent variable increases by one unit or from zero to one, holding other249

independent variables at their sample means.

250

For AdaptInd, a median regression is employed to characterize the relationship with the same251

set of independent variables we use in equation (3). The median regression is known to be preferred252

over the mean-based regression for characterizing a nonnormally distributed dependent variable in253

relation to some independent variables (Sarker et al., 2012, Hirose et al., 2023). Because AdaptInd

254

is identified not to follow a normal distribution according to the Shapiro-Wilk test, we judge that255

the median regression approach is proper in our analysis (Corder and Foreman, 2014, Khatun,

2562021). The regression specification is expressed as follows:

257

AdaptIndi = xi β K′ + ϵi258where AdaptIndi is the adaptation index the ith farmer takes, xi = (1, x1i , x2i , . . . , xℓi ) repre-259

sents a vectors of ℓ + 1 independent variables consisting of intercept, farm-size, CPInd, IWA,

260

cognitive, socioeconomic variables and the corresponding interaction terms, respectively. Finally261

β K = (β0K , β1K , . . . , βℓK ) is a vector of the coefficients associated with xi to be estimated through262

the least absolute distance estimation method, K = AdaptInd, and ϵi is an error term. Each263

coefficient is interpreted as a change in the AdaptInd median when one continuous (or dummy)

264

independent variable increases by one unit (or from zero to one), holding other variables constant.

265

The conceptual framework depicted in figure 3 and the regression specifications outlined in266

equations (3) and (4) enable us to determine the crucial factors for addressing the research question

267

and hypothesis in our study. We proceed to take the following steps. First, we conduct Mann-

268

Whitney non-parametric tests to determine some qualitative relations between the key variables

17

(4)

269

(Conover and Iman, 1981). Second, we apply Shapiro-Wilk tests to judge whether or not AdaptInd

270

is normally distributed (Shapiro and Wilk, 1965). Third, we estimate four regression models for271

each of AdaptNum and AdaptInd to be a dependent variable as robustness check: Model 1 includes

272

farm-size dummies as the independent variables along with an intercept. Model 2 additionally273

includes cognitive variables. Model 3 additionally includes cognitive and socioeconomic variables.

274

In model 4, to further characterize how the relationship between farm-size dummies and dependent

275

variables changes, we include interaction terms between farm-size variables and CPInd as well as

276

those between farm-size variables and IWA on top of the specifications in model 3.

277

4Results

278

Table 1 present the percentages of farms that implement certain adaptations by their sizes. For

279

instance, about 82 % of large-size farms use irrigation at night as an adaptation, and the percentages

280

for medium and small-size farms are 67.51 % and 55.56 %, respectively. About 89 % of large-

281

size farms are practicing deep tillage, while the percentages are 50.76 % and 29.17 % for medium282

and small-size farms, respectively. Only about 3 % of large-size farms are reported to implement283

adoption of different varieties that include crops resistant to drought, pest and heat stresses, and the284

percentages are 9.64 % and 10.42 % for medium and small-size farms, respectively. Table 1 reveal

285

that some adaptations, such as night irrigation, deep tillage, crop rotation, adjustment to sowing

286

date, cereal crop production and use of fertilizers, are popular as adaptation practices among farms

287

in Tajikistan (Construction of reservoirs and channels, drip irrigation, runoff harvesting, restoration288

of degraded lands and cover crops, are not popular). The results appear to reflect the cost of each289

practice, specific environment, climate and farming history and culture, demonstrating the wide290

variation of the percentages in most practices by farm sizes. Overall, large-size farms are dominant

291

with respect to the percentage in 16 practices out of 34 ones. On other hand, small-size farms are292

dominant only in 5 practices, and for the remaining 13 practices, clear-cut tendencies across farm293

sizes are not observed. Overall, the percentages of adaptation practices can be said to have some18

(a) Adaptation number(b) Adaptation index

Figure 4: Boxplots for the number of adaptations (AdaptNum) and the adaptation index (AdaptInd)

294

linkage with farm sizes.

295

Table 3 indicates that the total sample size is 799, while 261, 394 and 144 observations are296

collected for the large, medium and small-size farms, respectively. Farmers take 7.32 adaptations

297

on average and the median is 8. The averages (medians) for large, medium and small-size farms are298

8.41 (9.00), 7.09 (7.00) and 5.98 (7.00), respectively, implying that AdaptNum displays an upward299

trend as the farm size increases. Boxplots in figure 4a also confirm the same trend by farm sizes.

300

The average AdaptInd for farmers is 4.22, and the median is 4.40. The averages (medians) are 4.62

301

(4.97), 4.18 (4.01) and 3.60 (3.80) for large, medium and small-size farms, respectively. Thus, the302

statistics indicate that AdaptInd is also likely to increase with farm sizes, being in line with the303

trend in the boxplots (figure 4b). Therefore, the trends in AdaptNum and AdaptInd over farm304

sizes are considered consistent with each other on the basis of the summary statistics and figure 4.

305

To statistically assess the distributional differences of AdaptNum and AdaptInd among the farm306

sizes, we employ a nonparametric Mann-Whitney test for each pair of small-size, medium-size and307

large-size farms. The null hypothesis is that the distributions of AdaptNum (or AdaptInd) between

308

different-sized farms are the same. The results show that the null hypothesis is rejected for every

309

pair at 1 % level, implying that the distributions of AdaptNum (or AdaptInd) differ across farm

310

sizes.

311

The average climatic perception index (CPInd) for farmers is 8.28, and the median is 9.00. The
19

Table 3: Summary statistics of the variables
Farm-size dummy
Large-size farm
(N = 261)

Medium-size farm
(N = 394)

Small-size farm
(N = 144)

Overall(N = 799

8.41 (9.00)
2.61
2.00
18.00

7.09 (7.00)
2.53
0.00
16.00

5.98 (7.00)
2.59
0.00
10.00

7.32 (8.00)
2.71
0.00
18.00

4.62 (4.97)
1.66
0.17
9.87

4.18 (4.01)
1.87
0.20
9.5

3.60 (3.80)
1.99
0.00
9.09

4.22 (4.40)
1.86
0.00
9.87

8.42 (9.00)
1.89
0.00
10.00

8.36 (9.00)
2.05
0.00
10.00

7.79 (8.00)
2.19
0.00
10.00

8.28 (9.00)
2.04
0.00
10.00

4.21 (5.00)
1.07
1.00
5.00

4.04 (5.00)
1.23
1.00
5.00

3.89 (5.00)
1.27
1.00
5.00

4.06 (5.00)
1.19
1.00
5.00

6.35 (6.00)
2.94
0.00
17.00

4.24 (4.00)
2.67
0.00
15.00

3.83 (3.00)
2.79
0.00
12.00

4.86 (4.00)
2.97
0.00
17.00

3.10 (3.00)
0.78
2.00
4.00

2.91 (3.00)
0.81
2.00
4.00

3.01 (3.00)
0.77
1.00
4.00

2.99 (3.00)
0.79
1.00
4.00

3.25 (4.00)
1.09
1.00
5.00

3.43 (4.00)
0.96
1.00
5.00

3.34 (3.00)
0.78
1.00
5.00

3.35 (4.00)
0.98
1.00
5.00

4.00 (4.00)
1.38
1.00
9.00

3.10 (3.00)
1.11
1.00
7.00

3.02 (3.00)
1.23
1.00
8.00

3.38 (3.00)
1.30
1.00
9.00

0.92 (1.00)
0.26
0.00
1.00

0.69 (1.00)
0.46
0.00
1.00

0.44 (0.00)
0.49
0.00
1.00

0.72 (1.00)
0.44
0.00
1.00

2.38 (1.50)
3.05
0.00
26.00

1.77 (1.00)
2.99
0.01
40.00

1.38 (1.00)
1.31
0.00
8.00

1.91 (1.00)
2.83
0.00
40.00

9.57 (8.00)
5.36
3.00
40.00

9.24 (8.00)
4.51
2.00
30.00

7.97 (7.00)
3.81
2.00
25.00

9.12 (8.00)
4.72
2.00
40.00

0.09 (0.00)
0.29
0.00
1.00

0.22 (0.00)
0.41
0.00
1.00

0.32 (0.00)
0.47
0.00
1.00

0.19 (0.00)
0.39
0.00
1.00

Dependent variables
Adaptation number (AdaptNum)
Mean (Median) a
SD b
Min
Max
Adaptation index (AdaptInd)
Mean (Median)a
SDb
Min
Max
Independent variables
Cognitive variables
Climatic perception index (CPInd)
Mean (Median)
SD
Min
Max
Farming experience
Mean (Median)
SD
Min
Max
Information access
Mean (Median)
SD
Min
Max
Education
Mean (Median)
SD
Min
Max
Socioeconomic variables
Irrigation water availability (IWA)
Mean (Median)
SD
Min
Max
Equipment and services
Mean (Median)
SD
Min
Max
Income source (base group = not agriculture)
Mean (Median)
SD
Min
Max
Distance to plots
Mean (Median)
SD
Min
Max
Family size
Mean (Median)
SD
Min
Max
Gender (base group = male)
Mean (Median)
SD
Min
Max
a Median in parentheses.
b SD stands for standard deviation.

Notes: Large-size farm (farm sizes > 3 ha), medium-size farm (3 ha ≥ farm sizes ≥ 1 ha),smallsize farm (farm sizes < 1 ha).

20

312

averages (medians) are 8.42 (9.00), 8.36 (9.00) and 7.79 (8.00) for the large, medium and small-

313

size farms, respectively, meaning that farmers tend to have high CPInd as farm size increases.

314

The averages (medians) of irrigation water availability (IWA) for large, medium and small-size

315

farms are 3.25 (4.00), 3.43 (4.00) and 3.34 (4.00), respectively. The results show that there are no316

considerable differences in average or median IWAs across farm sizes. The cognitive variables,

317

such as farming experience and information access, have a tendency to increase with farm sizes,

318

while education does not show such a tendency (see rows associated with “cognitive variables” in319

table 3). The socioeconomic variables, such as equipment and services, income source, distance to320

plots and family size, except for gender show an upward trend as the farm size increases (see rows

321

associated with “socioeconomic variables” in table 3). Overall, the summary statistics demonstrate322

that farmers are homogeneous in relation to education and IWA, while they are heterogeneous in323

relation to AdaptNum, AdaptInd, CPInd, farming experience, information access, equipment and324

services, income source, distance to plots, family size and gender.

325

Table 4 reports the estimated coefficients, marginal effects and their respective standard errors

326

of the independent variables on AdaptNum in Poisson regression models. The results underscore327

the robustness of the key variables across all four regression models, characterizing the relation-

328

ships with AdaptNum. For instance, the marginal effects of the medium and small-size farms on

329

AdaptNum are negative and statistically significant at 1 % level across all models. The results

330

demonstrate that medium and small-size farms do not adapt as compared to large-size farms. The

331

cognitive variables, such as CPInd, farming experience and information access, show positive re-332

lationships with AdaptNum, aligning with existing literature (Islam et al., 2016, Dang et al., 2019,333

Ajani and Geest, 2021, Fischer et al., 2022, Ewalo and Vedeld, 2023). We also observe that so-

334

cioeconomic variables, such as IWA, equipment and services, income source, distance to plots and335

family size, are significant. Past studies suggest that IWA is crucial in semiarid and arid regions

336(Sorg et al., 2014, Ndamani and Watanabe, 2015, Golla, 2021, Sharofiddinov et al., 2024), and337

equipment and extension services play vital roles in agricultural production and farmers’ adapta-

338

tions (Fahad and Wang, 2018, Umunakwe et al., 2022). Interaction terms between farm-sizes and

21

22

*** significant at 1 % level** significant at 5 % level* significant at 10 % level
Standard errors are in parentheses

Constant
Sample size
Climatic zones
Likelihood-Ratio

Small-size farm × IWA

Medium-size farm × IWA

Small-size farm × CPInd

Interaction terms(base group = Large-size farm)
Medium-size farm × CPInd

Gender (base group = male)

Family size

Distance to plots

Income source (base group = not agriculture)

Equipment and services

Socioeconomic variables
Irrigation water availability (IWA)

Education

Information access

Farming experience

Cognitive variables
Climatic perception index (CPInd)

Small-size farm

Farm-size variables
Farm-size dummies(base group = Large-size farm)
Medium-size farm

2.13***
792
No
79.92***−0.17***(0.03)−0.34***(0.04)

CoefficientME−1.31***(0.21)−2.43***(0.27)

Model 1

1.47***
771
No
138.68***0.05***(0.01)
0.04***(0.01)
0.01**(0.01)−0.01(0.02)−0.13***(0.03)−0.26***(0.04)

CoefficientME

0.33***(0.06)
0.32***(0.08)
0.09***(0.03)
0.03(0.11)−0.96***(0.21)−1.87***(0.27)

Model 2

1.21***
742No
180.09***0.04***(0.01)
0.03***(0.01)
0.03(0.03)−0.02***(0.01)
0.01***(0.002)0.01(0.04)

0.04***(0.01)
0.03***(0.01)
0.01**(0.005)0.002(0.02)−0.11***(0.03)−0.23***(0.05)

CoefficientME

0.33***(0.10)
0.24***(0.08)
0.23(0.25)−0.14***(0.04)
0.06***(0.02)
0.04(0.24)

0.31***(0.06)
0.25***(0.09)
0.07***(0.03)
0.01(0.13)−0.86***(0.26)−1.64***(0.34)

Model 3

1.40***
742
Yes
204.33***0.01(0.02)
0.03(0.02)
0.01(0.03)
0.11**(0.05)

0.03(0.02)
0.04***(0.01)
0.06*(0.03)−0.02***(0.01)
0.01***(0.003)−0.001(0.04)

0.03*(0.01)
0.04***(0.01)
0.01**(0.005)0.01(0.02)−0.27(0.19)−0.86***(0.26)

CoefficientME

0.37***(0.11)
0.26***(0.08)
0.47*(0.26)−0.11***(0.04)
0.06***(0.02)−0.01(0.27)

0.27***(0.06)
0.27***(0.09)
0.08**(0.03)
0.05(0.13)−1.14***(0.27)−1.83***(0.35)

Model 4

Table 4: Estimated coefficients of the independent variables on the number of adaptations (AdaptNum) in the Poisson regressions

339

IWA are found to indicate the heterogeneous effects on AdaptNum, implying that AdaptNum by340

small-size (medium- or large-size) farms is sensitive (insensitive) to IWA with a positive associa-

341

tion.

342

Table 5 reports the estimated coefficients, their corresponding standard errors and the statistical343

significance level of the independent variables on AdaptInd in median regression models. The

344

details of the median regression results for AdaptInd shall be discussed and interpreted as compared345

to those for AdaptNum, because the AdaptInd is argued to be one of the most appropriate and346

credible measurements for farmers’ adaptations in literature (Below et al., 2012, Koirala et al.,3472022). The estimated coefficients of medium-size farms on AdaptInd are statistically significant

348with a negative sign in models 1, 2 and 3 at 1 % level. Likewise, the estimated coefficients of349

small-size farms on AdaptInd are statistically significant at the 1 % level with negative sign across350

all models. The results indicate that medium- and small-size farms tend not to adapt by 0.52 ∼351

0.96 and by 0.78 ∼ 4.52 on the median AdaptInd, respectively, as compared to large-size farms,

352

holding other variables constant. Overall, the findings suggest that both medium and small-size

353

farms tend not to implement adaptations by the index as compared to large-size farms.

354

The coefficients of some cognitive variables, such as CPInd, farming experience, information355

access and education, are statistically significant at 1 % to 10 % level at least in some models (ta-356

ble 5). The results reveal that farmers tend to increase AdaptInd by 0.12 ∼ 0.15 when their CPInd

357

increases by one unit. Previous studies find that CPInd plays an important role in farmers’ adapta-

358tion responses (Deressa et al., 2011, Abid et al., 2016, Koirala et al., 2022), being consistent with359

the result. A year of farming experience tend to induce farmers to raise AdaptInd by 0.11 ∼ 0.17360

AdaptInd, and it is in line with previous studies that show the positive impact on adaptation deci-

361

sions (Fadina and Barjolle, 2018, Ado et al., 2020). Farmers are found to decrease AdaptInd by362

0.17 ∼ 0.21 as their education level increases by one unit. The negative relationship is not surpris-363

ing in Tajikistan in that most educated farmers have alternative occupations, inducing themselves364

to take adaptations from nonagricultural income-generating activities. Other key socioeconomic

365

variables, such as income source and family size, are identified to be significant in some models,23

Table 5: Estimated coefficients of the independent variables on the adaptation index (AdaptInd)
in the median regressions
Adaptation index (AdaptInd)

Farm-size variables
Farm-size dummies(base group = Large-size farm)
Medium-size farm
Small-size farm

Model 1Model 2Model 3Model 4−0.96***(0.18)−1.17***(0.24)−0.65***(0.20)−0.89***(0.26)−0.52***(0.19)−0.78***(0.26)−0.11(1.01)−4.52***(1.27)

0.15***(0.05)
0.17**(0.07)
0.05*(0.03)−0.21**(0.11)

0.12***(0.04)
0.13*(0.07)
0.03(0.03)−0.17*(0.10)

0.06(0.08)
0.11*(0.06)
0.02(0.03)−0.09(0.09)

0.06(0.08)
0.08(0.06)
0.36*(0.19)−0.04(0.04)
0.04**(0.02)
0.14(0.20)

0.02(0.11)
0.06(0.06)
0.12(0.18)−0.04(0.03)
0.04**(0.02)
0.10(0.19)

Cognitive variables
Climatic perception index (CPInd)
Farming experience
Information access
Education
Socioeconomic variables
Irrigation water availability (IWA)
Equipment and services
Income source (base group = not agriculture)
Distance to plots
Family size
Gender (base group = male)Interaction terms(base group = Large-size farm)
Medium-size farm × CPInd−0.19(0.09)
0.28**(0.11)
0.01(0.15)
0.51**(0.24)

Small-size farm × CPInd
Medium-size farm × IWA
Small-size farm × IWA
Constant
Sample size
Climatic zones
Pseudo R-squared

4.97***
785
No
0.04

*** significant at 1 % level** significant at 5 % level* significant at 10 % level
Standard errors are in parentheses

24

3.02***
766
No
0.06

2.36***
737
No
0.08

2.76***
737
Yes
0.08

366

following our expectations and intuitions. The two factors appear to contribute to farms’ adaptation367

capacities, facilitating active engagement for adaptations (Assefa and Gebrehiwot, 2023).

368

We examine the interaction effects between farm-sizes and CPInd as well as between farm-369

sizes and IWA on AdaptInd. To this end, the interaction terms are included and estimated in Model370

4, enabling us to derive the predicted median AdaptInd over CPInd or over IWA for each farm size.

371

Model 4 shows that the coefficient of the interaction terms between small-size farms and CPInd is372

statistically significant at 5 % level. It implies that small-size farms tend to raise AdaptInd by 0.34

373(= 0.06 + 0.28), when their CPInd rises by one unit. Similarly, the coefficient of the interaction

374

terms between small-size farms and IWA is statistically significant at 5 % level, implying that375

small-size farms take additional AdaptInd by 0.53 (= 0.02 + 0.51), when their IWA improves by376

one level. Overall, adaptations by small-size farms can be interpreted to be highly dependent on

377

CPInd as well as on IWA with positive associations, while large-size and medium-size farms are378

not. The results suggest some possibility that the interaction effects of farm sizes with CPInd and379

with IWA practically influence AdaptInd in Tajikistan farming.

380

Based on the estimation results from model 4, we compute and graph the predicted median381

AdaptInd over CPInd and IWA for each farm size in consideration to the interactions (figure 5).

382

The predicted median AdaptInd for each of large, medium and small-size farms in figure 5a demon-383

strates that the intercepts and slopes are idiosyncratic between small-size farms and other-size ones

384

(medium- and large-size farms). In particular, the intercepts for the large and medium-size farms

385

are higher than that for the small-size farms, and the predicted median AdaptInd has a upward386

slope over CPInd only for small-size farms. On the other hand, it is evident that the predicted me-

387

dian AdaptInd is not sensitive to CPInd for each of medium and large-size farms, being practically388

flat. Likewise, the same tendencies of the predicted median AdaptInd over IWA are found across389

farm sizes (figure 5b), presenting that adaptations by small-size (large and medium-size) farms390

are sensitive (insensitive) to IWA with a positive slope. Overall, figures 5a and 5b are interpreted391

to suggest that small-size farms in Tajikistan are vulnerable to or fragile against climate change392

through a decline in adaptations, especially when they have neither proper climate perceptions nor

25

(a) Predicted AdaptInd over CPInd by farmers across farm sizes(b) Predicted AdaptInd over IWA by farmers across farm sizes

Figure 5: Predicted adaptation indices (AdaptInd) over climatic perception index (CPInd) andirrigation water availability by farmers across farm sizes

26

393

sufficient water availability. It must be noted that large- and medium-size farms do not display

394

such a tendency, and we conjecture that they possess some advantages for adaptations reflecting

395

farming culture and history that have prevailed in Tajikistan as compared to small-size one.

396

We summarize the statistical and econometric results related to AdaptNum and AdaptInd,

397

providing the answers to our research question and hypothesis. As outlined in our conceptual

398

framework, AdaptNum and AdaptInd are influenced by cognitive factors, socioeconomic ones and399

their interactions. The results demonstrate that farm sizes, CPInd, farming experience, informa-

400

tion access, IWA and family size influence both AdaptNum and AdaptInd in a robust manner.

401

In particular, the summary statistics, boxplots and regression results uniformly suggest that farm402

sizes are key variables to characterize adaptations, that is, adaptations tend to increase with farm403

sizes. Estimated coefficients on the interaction terms and the associated graphs show that large and404

medium-size farms stably take adaptations, irrespective of CPInd and IWA, while small-size farms

405

are sensitive to the factors or vulnerable against climate change. It means that small-size farms go

406

through a decline in adaptations or do not have an ability to adapt, especially when they do not407

have good CPInd or sufficient IWA. The results may be due to traditional farming practices during

408

the Soviet period “Kolkhoz” and “Sovkhoz” where Tajikistan farmers are familiar with agricultural

409

production and management under large and medium-size farms, adapting to climate change. On

410

the other hand, Tajikistan farmers in small-size farms are known to suddenly become the owners

411

of newly allocated small agricultural plots, and they do not receive proper instructions or training

412

regarding how to manage and make productions under small farming (Van-Assche et al., 2013,

413

Shtaltovna, 2016). Therefore, such farmers may not posses any embodied knowledge, skills and414

abilities to adapt particularly under the lack of their climate perceptions and water availability. It

415

is our belief that the statistical analyses present coherent results with one another, well reflecting

416

what is going on farmers’ adaptations by farm sizes in Tajikistan.

417

Land fragmentation is a growing worldwide trend which is known to be influenced by external418

factors, such as population growth, economic uncertainties and policies, bringing farm sizes to be419

small over time (Tan et al., 2006, Hartvigsen, 2014, Sharofiddinov et al., 2024). In some coun-27

(a) The proposed plot and farm sizes in intermediate step(b) The proposed plot and farm sizes in final step

Figure 6: Proposed plot and farm sizes in intermediate and final steps of land consolidation.

28

420

tries, land fragmentation is empirically established to be beneficial as well as to increase farmers’421

adaptation capabilities in agriculture (Kabubo-Mariara and Mulwa, 2019, Ntihinyurwa et al., 2019,

422

Koirala et al., 2022). In general, however, the superiority between large and small sizes or between

423

large-scale and small-scale business operations is not clear-cut in terms of profitability and adapta-

424tion capability, remaining inconclusive depending on the types of management practices, cultural

425

contexts and histories (Revilla and Fernández, 2012, Hollender et al., 2017). Against this back-426

ground, our research indicates that land consolidation is recommended to enhance agricultural

427

adaptations in Tajikistan and other countries who face the similar situations of old infrastructures428

and poor water availability due to the cultures and histories. For instance, it shall be effective to es-429

tablish community-based management, such as enterprises or shareholders, through consolidating

430

small-size farms and their fragmented plots. Figures 6a and 6b depict two examples for possible

431

steps. Figure 6a suggests that approximately 80 medium and small-size farms in 120 ha with 237

432

plots can be consolidated to form five enterprises (shareholders) that consist of 12 plots on the basis433

of the plot structures in the pre-reform periods. Figure 6b further suggests that the five enterprises434

can be merged into one in 120 ha with 12 plots. The suggestions are expected to enable farmers to435(i) rotate and diversify their crops, (ii) have access to water irrigation systems and roads, (iii) in-

436

crease water availability, (iv) improve efficiency of machinery and (v) reduce unnecessarily roads,

437

ditches and farm boundaries under the current water infrastructures in Tajikistan. Alternatively, if

438

such land consolidation is considered unfeasible, small-size farms should receive proper instruc-439

tions for climate perceptions or further provision of suitable irrigation infrastructures for sufficient

440

IWAs, increasing their adaptations.

441

5Conclusion

442

This study has examined how farm sizes influence adaptation responses in relation to irrigation-

443

related, cognitive and socioeconomic factors reflecting farming culture and history, hypothesizing

444that large-size farms adapt to climate change as compared to small-size ones in Tajikistan. We

29

445

utilize a questionnaire survey with 800 farmers on their adaptation responses, farm sizes, climatic

446

perceptions, irrigation water availability (IWA), cognitive and socioeconomic factors. The anal-

447

yses reveal that farm sizes, climatic perceptions and IWA are key determinants to characterize448

adaptations. The findings show that small-size farms do not adapt as compared to medium- and449

large-size farms, and the adaptations by small-size farms are highly dependent on their climatic

450

perceptions and IWA. Overall, this research establishes that large and medium-size farms take451

adaptations irrespective of climatic perceptions and IWA, while small-size farms are found to be452

sensitive to or vulnerable against climate change. Therefore, the results indicate that the ongo-453

ing land-fragmentation policy in Tajikistan should be reconsidered due to the losses in adaptation454

responses, as it has been drastically increasing the number of small-size farms. Otherwise, it is455

crucial to provide proper guidance for how to manage and adapt through improving small-size

456

farmers’ climatic perceptions and developing proper irrigation infrastructures to ensure their water457

availability.

458

We acknowledge some limitations of this study and provide directions for future research. Our

459

analyses do not incorporate long-term farmers’ adaptations in relation to climatic data. Our study

460

captures farm-level adaptations including their quantity and coverage areas in relation to various

461

factors and farm sizes with the cross-sectional data. Farmers’ adaptations may go beyond the462

results by analyzing the cross sectional data through a questionnaire survey. Thus, future studies

463

should collect panel data that include farmers’ adaptations and climatic data over several growing

464

seasons or years. By doing so, further countermeasures for increasing agricultural adaptations shall465

be identified. Moreover, we do not associate farmers’ adaptations with their performances, such as

466

profits or productions in the analyses. In future, it is recommended to examine the relation between

467

some performance variables and adaptations in relation to farm sizes within a single framework,

468

utilizing switching regressions. This type of researches shall contribute to our understanding of469

how the sizes matter for not only adaptations but also performances along with the relation between

470

the two. Despite the limitations, we believe that this study demonstrates a clear evidence, that is,

471

farm sizes are keys for adaptations and land fragmentation is not necessarily recommended in the

30

472

context of Tajikistan. The evidence shall be considered an important initial step for clarifying

473

the functions among farm sizes, cognitive and socioeconomic factors in agriculture as well as for474

countering adverse effects from climate change on food security in countries facing the same types

475

of situations as in Tajikistan.

31

476

Acknowledgement: We would like to thank the anonymous reviewers, Makoto Kakinaka,

477

Sulton Rahimzoda, Kostya Ovsiannikov, Khatun Asma, Mostafizur Rahman and Mirzoqurbon

478

Pochoev for the helpful comments and support. The authors specially thank the Ministry of En-

479

ergy and Water Resources, the Agency for Land Reclamation and Irrigation and Agency of civil

480

service under the President of the Republic of Tajikistan for their support. We are also grateful for481

the financial support from the Japanese Society of Promotion of the Science as the Grant-in- Aid

482

for Scientific Research B (19H01485), Research Institute for Future Design, Kochi University of

483

Technology and the Project for Human Resource Development Scholarship by Japanese Grant Aid

484(JDS).

32

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