Perceptions on Climate Change and Satisfaction on Adaptive Measures:

Farmer Field Evidence from Punjab, Pakistan

ZEESHAN SHABBIR RANA1, INTIZAR HUSSAIN2, ABDUL SABOOR3,

MUHAMMAD USMAN4*, SHUMAILA SADIQ5, NASIR MAHMOOD3*, LAL KHAN ALMAS6*

1Additional Secretary, Prisons, Home Department,

Government of the Punjab, Lahore,

PAKISTAN

2Islamic Development Bank,

Jeddah,

KINGDOM OF SAUDI ARABIA (KSA)

3Department of Economics & Agricultural Economics,

PMAS-Arid Agriculture University, Rawalpindi,

PAKISTAN

4Department of Management Sciences,

National University of Modern Languages (NUML, Rawalpindi),

PAKISTAN

5Department of Economics,

The Government Sadiq College Women University, Bahawalpur,

PAKISTAN

6Paul Engler College of Agriculture & Natural Sciences,

West Texas A&M University, Canyon, TX 79016,

USA

*Corresponding Authors

Abstract: - Climate change poses a serious threat to the agrarian economy of Pakistan. Future agriculture

productivity of the country can only be secured through the adaptation of climate change strategies. This

research is designed to investigate the farmers’ perceptions of climate change and their satisfaction with the

adaptation measures in the Punjab province of Pakistan. The questionnaire-based data was collected in 36

districts, from 360 respondents through the field survey. Both random and convenient sampling techniques

were employed. For empirical analysis, a Multinomial Logistic regression model was operated. The results

indicate that an increase in per-hectare yield lessens the farmer’s vulnerability to climate change. This research

found that the farmers observed that changing precipitation patterns, extreme climate events, mutable sowing

and harvesting time, temperature variation, night temperature, and traditional crop varieties are key vulnerable

factors of climate change. These may create an alarming situation for agriculture productivity in the province. It

is registered that farmers are not satisfied with adaptation measures particularly concerning heat-resistant and

drought-resistant varieties. Agriculture extension services could not deliver optimally to protect the agriculture

output from climate vulnerability. The results show that farmers are not satisfied with the performance of

climate-resilient and research institutions. It is recommended that the government, research institutions, and

climate-resilient institutions design new sowing and harvesting patterns, new seed varieties, new climatic

zones, and alternative crop switching. The whole paradigm of extension services needs to be modernized and

mechanized with the wider application of ICTs. The extension department should timely disseminate the

climate information and educate the farmers on climate resilience and adaptation.

Key-Words: - Climate Change, Climatic Adaptive Measures, Farmers perception, farmers Satisfaction,

Agriculture Output, Multinomial Logistic Model

Received: March 9, 2023. Revised: August 11, 2023. Accepted: October 7, 2023. Published: October 17, 2023.

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DOI: 10.37394/232015.2023.19.102

Zeeshan Shabbir Rana, Intizar Hussain,

Abdul Saboor, Muhammad Usman,

Shumaila Sadiq, Nasir Mahmood, Lal Khan Almas

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1 Introduction

The climate change is considered as a global

phenomenon. It has extensive implications across

the time and regions, [1]. The developed countries

are situated at higher latitudes. They will get

benefits from the current changes in temperature

and heat waves if prolonged for twenty to thirty

years, [2], [3]. The yearly mean temperature has

expanded to around 0.5 C0 globally. Different

regions are already extremely affected due to

climatic variation. Developing countries are most

prone to climate change (CC) though they have less

than a 10 percent contribution to global carbon

emission, [2]. According to climate scientists, the

impact of climate vulnerability is higher in

developing countries than that of developed

countries, [3], [4], [5]. The level of green

technology in developing countries is insufficient to

address the climatic challenges and enhance their

agriculture production.

As far as the dangerous climatic impact is

concerned, Pakistan is the 7th most vulnerable

country. She is dubbed as the hotspot of the world.

She is ranked 134th in environmental damages

(corban emission production), [6]. Currently, she is

experiencing climatic effects through a series of

floods and droughts that directly affect agriculture

productivity, [7], [8]. The discrepancy in rainfall

cycles and shifting temperature has negative

impacts on agriculture productivity. Additionally,

the changing weather is destroying agriculture

productivity, decimation of livestock herds, and

farmer’s livelihood, and creating food insecurity in

Pakistan, [9]. The climate changes have left wide-

running effects, influencing water accessibility, and

expanding recurrence of extreme climate events, [6].

Pakistan has rich natural resources, including

agricultural land, mineral deposits, and gas reserves.

Primarily, the agriculture sector contributes 19

percent to Gross Domestic Product (GDP) and

provides 37 percent employment, [10]. In Pakistan,

most of the agricultural land is cultivated through

surface water but fewer areas are dependent on

rainfall. The changing pattern of rainfall directly

affects agriculture productivity and thus the GDP of

the country. The government of Pakistan established

and implemented the climate change policy in 2012

nationally. The immediate and effective purpose of

this policy was to prevent future environmental

damages such as soil erosion and deforestation,

[11]. Globally, Pakistan is ranked 18th out of 191

countries in the disaster risk index. This index has

been driven particularly at the national level by

exposure to flooding, earthquakes, and the risk of

internal conflict, [11]. The global disaster ranking

highlighted that Pakistan is the most vulnerable and

at a high risk of climate change. So Pakistan needs a

workable policy to avoid the bad impact of climate

change, adapt to climate change, and follow

mitigation wherever required, [9], [12].

The adaptation of climate change measures is

directly influenced by farmer’s perception of the

climate-resilient institutes and their satisfaction

level [2]. The Environmental Kuznets Curve (EKC)

suggests that initially, economic development

deteriorates the environment at a certain level. But

after a particular period, the economy begins

improving, and the environmental degradation

reduces. The results of the Kuznets hypothesis

reveal that the agriculture production infrastructure

required reconsideration of climatic measures to

avoid production inefficiency, [13], [14].

Additionally, the tremendous economic

development reduces agriculture productivity, land

conservation, land fertility, and environmental

efficiency, [5], [7]. The problem with over energy

consumption is that it affects the environment and

produces carbon emission gasses, [15], [16]. It was

found that economic growth has a positive impact

on environmental pollution, [17].

Similarly, in an important research, it has been

argued that energy consumption has a positive

contribution to environmental degradation and

economic growth in Pakistan, [18]. The increased

growth level has enhanced the environmental

degradation and thus validated the EKC hypothesis,

[19]. Another study, [20], found that environmental

damages like agriculture methane, agriculture

nitrous, and CO2 emission are a reaction to over-

energy consumption in Pakistan. Annually,

precipitation has demonstrated noteworthy

changeability over seasons in Pakistan. Average

rising temperature and ecological variation affect

agrarian crop planning and rainstorm precipitation.

Consequently, this will fundamentally affect the

agricultural production of water subordinate areas

and profitability, for example, energy and

horticulture, [21], [22]. Farmer’s eagerness and

adjustment capacity of agriculture framework relies

on variation in the atmosphere and see

vulnerabilities of extreme occasions, [5], [23]. The

Punjab government developed a climatic policy in

view of farmers' networking, extension services,

assessment programs, and collaboration with

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Zeeshan Shabbir Rana, Intizar Hussain,

Abdul Saboor, Muhammad Usman,

Shumaila Sadiq, Nasir Mahmood, Lal Khan Almas

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stockholders to develop appropriate climate policy

to overcome future climatic vulnerability. The

Punjab government is facing a dilemma on an

institutional alliance, which creates difficulties in

controlling the destructive impacts of CC, [6], [7],

[9], [24]. Farm-level adaptation measures involve

two categories: perceiving a variation in CC and

deciding whether to opt or not opt the useful

adaptation strategies, [4], [25], [26], [27].

The literature provides evidence that climatic

vulnerability affects agricultural productivity

globally, [3], [7], [8], [28], [29], [30]. In addition, a

list of studies exhibits that climatic variation has a

mixed effect. Some studies demonstrated that high

temperature is suited for the crop sector (especially

wheat), [23], [31], [32], [33]. Others suggested that

the changing climate is adversely affecting

agricultural productivity, [34], [35]. The adaptive

measures of climate change are meant to overcome

the farmer’s uncertainty of agriculture productivity

by introducing new cycles of sowing and harvesting

crops. It is imperative to realize how some policy

shifts may make changes in the pattern of the likely

impact of climate change on the agrarian economy

of the country. In this regard, the existing studies

focused on climate change effects on agriculture

yield, cereals, and disruption the food availability

and accessibility, which can be reduced through

pecuniary and precautionary measures to avoid

climatic vulnerability in Pakistan, [9], [33], [36],

[37], [38], [39], [40], [41], [42]. Based on the

existing research gap, the present study raises the

following research question: Do the farmers have

knowledge about climate change? What is the

farmer’s perception of climate change in the Punjab

province of Pakistan? What is the impact of climate

change on agricultural productivity? Do the farmers

are following the adaptation measures for climatic

vulnerability? Do the farmers are satisfied with

climate change adaptation measures in Punjab,

Pakistan?

1.1 Objectives of the Study

1. To investigate the farmer's knowledge about

climate change in Punjab Pakistan

2. This research examines the farmer’s

perception of Climate Change (CC) policy

and their satisfaction with adaptive

measures taken by the government of

Punjab, Pakistan.

3. This study investigates the impact of

climatic policy on agricultural productivity.

4. This study explores the role of climatic

vulnerability in agricultural output and

government actions to avoid production

inefficiency in Punjab, Pakistan. This

allows evaluation of the climatic policy

adaptation and its implementation

consequences on the agricultural business.

5. The implicit research idea is to arrive at

some policy framework for mitigation and

adaptation to climate change.

2. Review of Literature

2.1 Climate Change and Policy Development

According to the climate change profile of Pakistan,

the climatic changes have unexpected impacts on

productivity, affecting water accessibility,

agriculture efficiency, and extended recurrence of

harmful climatic occasions, [10]. In coming

decades, the CC-related common perils may

augment in seriousness and recurrence. Tending to

these crises involves an environmental variation to

be mainstreamed into a national approach and

methodology.

Farmers' willingness and capacity to adopt the

environmental framework depend on the availability

and capability of climatic knowledge. The gap is

found in terms of easy access and reliable

information to farmers. There is an inconsistency

between farmers' precipitation about climatic

vulnerability and actual atmosphere record, [23],

[42]. Training, farming background, landholding,

land property, expansion, participation, access to

CC adaptation, lack of extension services, and

access to new information are the key elements

affecting the adaptation stages. The carbon tax will

help in accommodating the policies and also in

environmental protection through inflicting

pollution taxes. Developing countries can reduce

environmental harmony through effective energy

consumption policies, emission taxes, and workable

policies to control environmental damaging factors,

[43], [44].

The literature shows that most agrarian

economies do not consider environmental variation

as a potential risk for agricultural output. The

farmers are not enthusiastic to adopt innovative

cultivation techniques and follow the adaptation

measures to climate change, [9], [12], [22]. The

newly developed cultivation techniques are

providing higher crop productivity and overcoming

the climatic challenges, [28]. Unfortunately,

environmental variations in Pakistan are genuine

and detrimental, but the question is, do the farmers

acknowledge it or not? Further, farmers adopt

appropriate measures to overcome climatic

vulnerability, [33], [45]. The main limitation in

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DOI: 10.37394/232015.2023.19.102

Zeeshan Shabbir Rana, Intizar Hussain,

Abdul Saboor, Muhammad Usman,

Shumaila Sadiq, Nasir Mahmood, Lal Khan Almas

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adaptation is the lack of a suitable functional climate

and financial policy for the farmers. Access to

innovation and availability of agriculture credit are

helpful in the form of adaptation measures for the

farmers, [46]. Further, farmer recognition and

education on environmental change, current

adaptation measures, and basic leadership

procedures are the fruitful factors for the adaptation

of climatic-resistant methodologies, [45]. The

necessary actions taken by the government are

helpful in empowering the farmers through versatile

climatic adaptive measures and providing an

enabling environment to cope with harmful climate

issues, [47].

The climatic vulnerability affects the

agribusiness and farm yield particularly, in South

Punjab, Pakistan. The potential production of rice,

wheat, sugarcane, and maize has been affected by

climatic changes in the last decade, [1]. The rising

temperature harms the crop's yield than the drop in

temperature during winter. Moreover, the erratic

pattern of rainfall has negative impacts on all crops

except wheat. The connection between CC

adaptation procedure and sustenance security is

positive while having a negative relationship

between environmental change and adaptation

techniques, [1]. Another study, [48], demonstrated

that a paradigm shift is required in research

endeavors, and research focused on climate change

on two heads. It is necessary to increase household

family resources and consciousness by bringing

down the expense of adaptation. Evaluating the

adaptation strategies, they found that aside from the

Climatic Change Arrangement of Nepal, none of the

strategies practiced in other South Asian Countries

is transboundary scale adaptation, [48], [49].

Therefore, a comprehensive policy should be

formulated that could avoid considering the

transboundary impacts of CC in collaboration with

other countries in the region.

2.2 Climate Change and Adaptations

Measures

Pakistan is a climate-vulnerable country and faces

extreme climatic events like droughts and floods,

[6]. It has been examined in six Khyber

Pakhtunkhwa (KPK) districts the adaptation

measures opted for by the farmers to nullify the

impact of adverse climatic shocks, [39]. The

findings revealed that climate change generates

subsequent issues in the agriculture sector of

Pakistan, such as the loss of soil fertility, water

scarcity, changes in sowing and harvesting patterns,

and crop diseases. The climatic variation affects the

world, especially the South Asian agriculture sector,

where the adaption and mitigation tendency among

the farmers is poor, [32], [40]. The instruments

affecting the climate are GHGs, which depend upon

human-related activities, deforestation,

transportation, industry, agriculture, urbanization,

livestock, and energy uses. The farmer endures that

CC accumulated the sowing and harvesting periods

are changed, [39], [50]. Most farmers indicate that

crop diseases come into the picture due to warm

temperatures. However, a timely adaptation strategy

by agrarians in the respective areas is helpful for

variation in the crop calendar.

Climatic adaptation affects the production gain

and the farmer bears the cost, [51]. The productivity

gains have a significantly positive contribution for

rice producers who adapted but trifling for wheat

producers. The small farmers have utilized

Ecosystem-based adaptation strategies due to

climatic variation while the ecosystem is disturbed,

[52]. The farmers who have social capital and

institutional access and availability used more

adaptation strategies, and small farmers used

adaptation strategies, but still a need to improve

adaptation through government policies. Erratic

patterns of rainfall, temperature, and CC have

altered the harvesting and sowing period of major

crops in Punjab and thus substantially influenced the

farm incomes of poor farmers are adversely which

are affected by these CCs, [6], [23], [38]. The small

farmers have to either shift to innovative crop

varieties to maintain their level of income or need

extra credit to cope with the issue, and both are

beyond the reach of poor farmers. The systematic

review analysis on adaptations and climate changes,

[53] focused on the adaptation strategies and

concluded that the climatic changes by meta-

analysis and systematic review method. The first

suggestion for the qualitative study is to make an in-

depth analysis and explanation of farmers’

adaptation and decision-making, [33], [48]. Global

livestock will be doubled by 2050 and climate

change is a main threat to meat production because

of low-quality crop feed and forage, availability of

water for animals, livestock diseases, biodiversity,

and animal reproduction, [54]. Therefore, livestock

production transformation into sustainability

requires assessments related to the use of mitigation

and adaptation measures and policies that could

support and facilitate the CC implementation, [55].

Climate change directly affects the crop sector and

indirectly it affects the livestock sector. Directly, the

area under cultivation is declined, crop productivity

is reduced, cultivation cycle is diversified which

increases the farmer's uncertainty about sowing and

harvesting of the crops, [56], [57], [58]. Some

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researchers, [59], have found that climatic changes

significantly affect the mean temperature,

precipitation, rainfall pattern, and deforestation.

Similarly, [37], found that people are well aware of

climate change and farmers are taking adaptations to

control and reduce climate vulnerability.

3 Materials and Methods

3.1 Multinomial Logistic Regression

For microdata analysis, Multinomial Logistic

Regression (MLR) is relatively more suitable when

the targeted variable has more than two choices, and

the explanatory variables are of any type:

continuous, ordinal, or nominal. The MLR model

does not include categorical predictors and involves

the coding strategy. Categorical predictor variables

may be entered into the equation directly as key

factors in the MLR dialog menu box, [60], [61],

[62], [63]. The ordered logit model follows the

normal distribution, through which it is easy to

interpret by using the odds ratios. The multinomial

regression analysis has utilized the maximum

likelihood method, [64]. For categorical analysis,

we have the following model:

Yij= 1, if the respondent i chooses alternative j (j=2,

3, 4 and 5).

In this equation, 1 represents strongly disagree and 5

represents strongly agree.

        

       (1)

In the above equation, ‘y’ is an unobservable

variable, ‘x’s’ are explanatory variables and uij is a

residual term. Whereas the term i" represents the

different cross-sections. For multiple response

categories, the dummy variable follows order or

rank, and ordered logit and probit models should be

applied when choices are more than two such as

strongly disagree, disagree, neutral, agree, and

strongly agree. Such models presume that the

experimental Di is determined through Di as

follows:

      (2)

        (3)

        (4)

     (5)

In this case, value 1 is for the lowest response

(strongly disagree), 2 represents the incremental

response (disagree), and so on 5 represents the

‘strongly agree’ scale. In this study, the coefficient

was estimated by adopting the MLR in SPSS

software. The coefficient value indicates the logistic

estimates for each predicted variable, with an

alternative category of the estimated variables, [65],

[66]. Therefore, the alternative categories do not

refer to the response category. The MLR coefficient

represents the expected value of responsive change

in logistic probability in each predictor. The MLR

model anticipates the odds and risk and uncertainty

analysis of response categories of predictor and

explained variables. The estimated result also

displays the Wald statistic, standard error, DF, Sig.

(p-value); as well as the odds ratios (Exp (B)). The

Wald test with its associated p-value is applied to

evaluate the MLR coefficient, whether it is or not

different from one. The predictor variable is

expected to increase the MLR odd-ratios and risk

from response variables, to display greater than 1.0.

The predictor decreases the MLR will have Exp(B)

values less than 1.0, while those predictors that do

not affect the MLR display an Exp(B) of 1.0, [60],

[61], [62], [63], [64], [65], [66].

3.2 Econometric Model of Farmer

Perception of Climatic Change

Regression analysis is essential for economic

dependency among economic phenomena. The

multinomial logit model is applied for survey

analysis where the dependent variable has more than

two responses from the respondents. MLR is an

advanced form of binary logistic regression, which

provides us with the coefficient matrix and odd

ratios of the selected model, [61], [66]. The

empirical model to measure the farmer’s perception

of CC is as follows:

              

(6)

In the above model, yij is a dependent variable

which represents ‘Farmer's perception about

agriculture vulnerability to CC’ and bo is the

intercept, and b1, b2, . . . . . . . b9 are slope

coefficients. While xij are cross-sectional

independent variables such as ‘Per Hectare Yields’,

‘Increase in Temperature’, ‘Night Temperature’,

‘Harvesting Time’, ‘Crop Varieties’, ‘Extension

Services’, ‘Women Farmers’, ‘Small Farmers

Vulnerability’ and ‘Soil Fertility’ respectively.

3.3 Model of Farmers' Satisfaction with

Climatic Measures

The MLR for farmer’s satisfaction with climatic

adaption measures is designed to identify the

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Zeeshan Shabbir Rana, Intizar Hussain,

Abdul Saboor, Muhammad Usman,

Shumaila Sadiq, Nasir Mahmood, Lal Khan Almas

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farmer’s response to government policy adaption.

The farmer’s satisfaction level about climate change

resilient institutions is investigated through Likert

Scale data. MLR is an advanced form of binary-

logistic regression which provides us with the

coefficient matrix and odd ratios to interpret the

dependency relationship among variables, [61],

[64], [65], [66].

             

 (7)

In the above model, yij is a farmer's satisfaction

with climate change resilient institutions in Punjab

bo is the intercept, and b1, b2, . . . . . b9 are slope

coefficients. While, there are independent variables

such as the Punjab Government, Laws &

Regulations, Research Institutes, Weather

Mechanisms, NGOs, International Organizations,

Community Interventions, Climate Funds, and

Public-Private Partnership (PPP) respectively.

3.4 Data Framework

This study examines the farmers' perception of CC

and their satisfaction with adaptation measures

taken by the government of Punjab, Pakistan. For

research objectives, the microdata were collected

through a field survey from farmers. The

questionnaire consists of three sections covering

demographic, and agrarians’ perceptions regarding

climatic changes, and the third section covers the

farmers' satisfaction regarding adaptation measures.

The questionnaire-based field survey was conducted

in 36 districts of Punjab, Pakistan. 10 questionnaires

were filled through farmer interviews from each

district (Table 1, Appendices). The data was

collected from small, medium, and large-scale

farmers. This research was based on a designed

questionnaire and data was collected through a field

survey. Equal weight was provided to all districts

because a suitable climate is important for all

farmers and implementation of the climatic policy

has equal importance. As per conventional wisdom,

around 10 respondents were selected from each

district of Punjab, Pakistan. Literature justifies and

supports such a number of farmers. The sample size

consists of 360 observations collected randomly.

The survey was carried out through a multistage

sampling technique, and respondents were selected

through a convenient sampling technique. Collected

data is the best representative of the population in

terms of statistical specification.

3.5 Sampling Framework

In Punjab, there are 5,249,800 agriculture farms

located the 36 districts (Census of agriculture 2016-

17). The targeted population is farm managers and

we considered the 5,249,800 farms as the

agriculture farmer’s population. The rationale of the

unbiased selection of agriculture farms is to get the

climatic impact on each farm in all 36 districts of

Punjab Pakistan. In order to get the true outcome

and farmers’ perception and satisfaction about

climatic challenges in Punjab, we gave them equal

importance and selected the 10 responses from each

district. This research adopted a snowball sampling

technique to collect the data from the respondents.

The determined sample size is a representative

sample from the agriculture farm managers, which

is calculated according to the [67], sample

calculator. This study took a 0.06 percent precision

level with a 94 percent confidence interval.

  

󰇛󰇜 (8)

Where n is the sample size and n is the size of

the population and precision level are denoted by (n)

and (e) respectively. Based on study objectives, a

rigorous literature review has been performed to

identify the problem-relevant variables for said

study and to finally incorporate those in the form of

askable statements in the questionnaire. Keeping in

view the study area’s specific agro-climatic

conditions, we pre-tested our designed questionnaire

by conducting a pilot survey of 10 percent of the

total sample size via interviewing one farmer from

each district i.e., 36 farmers in total. Contingent our

the field insights we got from interviewing these 36

farmers, we rectified our questionnaire by excluding

the irrelevant questions and including the most

relevant ones in our questionnaire. Furthermore, we

have also interviewed the progressive farmers from

targeted communities as well as field experts from

the local agriculture department to further validate

our designed questionnaire. Lastly, the farmers

interviewed during the process of pre-testing and the

progressive farmers were not been included in our

final sample of 360 farmers.

4 Results and Discussion

4.1 Summary Statistics

The results of descriptive statistics presented in

Table 2 (Appendices) show that the average

respondent age is 44 years, which indicates that the

survey was carried out by knowledgeable and

experienced farmers, who are well aware of the

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agriculture business. The farmer’s average

landholding is 18.42 acres, so the survey is

classified into large-scale, medium-scale, and small-

scale farmers. The questionnaire consists of 15

instruments regarding the farmer's perception of

climatic changes and 10 questions regarding

farmers' satisfaction with climate institutions. The

maximum response on all instruments is 5 (strongly

agree), except four questions and the minimum

response of all instruments is 1 (strongly disagree).

The ‘lack of precipitation’ contains the maximum

response of the farmers on a scale of 4, which

means that farmers do not strongly disagree about it

and are considered (lack of precipitation) an

essential instrument for CC.

4.3 Results of Farmers' Perception of

Climatic Vulnerability

4.3.1 Model Processing Summary and Goodness

of Fit

The model processing summary results show that 87

percent of farmers strongly agreed that agricultural

productivity is highly vulnerable to CC in Punjab,

Pakistan (Table 3 Appendices). Around 7.8 percent

of respondents are not aware of CC and its effect on

agricultural productivity. Further 5 percent disagree

about the climatic vulnerability on agricultural

production. The results of the processing summary

concluded that farmers in Punjab are knowledgeable

about climate change and its vulnerable effects on

agriculture output, [9], [28], [31].

The goodness of fit of the model is supportive

and predicts policy messages. The results of the

efficiency and validity of estimates are given in

Table 4 (Appendices). The estimated value of the

Chi-Square of likelihood measure is high (119.52)

with zero probability value, which rejects the null

hypothesis significantly. So, the estimated model is

well-fitted, and estimates are good for drawing

messages for policy purposes. The value of Chi-

Square is high, which shows the independent

variables have a strong influence on the probability

of agriculture vulnerability to CC. Similarly, the

estimated value of Pearson is highly significant (at

0.036 probability value) and Deviance is highly

insignificant (at 1.00 probability value) which is a

recommendation of the goodness of fit of the

estimated model. In a similar line, the estimate of

Pseudo R-Square (Nagelkereke test) is 0.317, which

shows the estimated model is well-fitted. The value

of Pseudo R-Square shows that 31.7 percent

variation in agriculture climatic vulnerability is

because of selected variable, while other 68.3

percent vulnerability of climate change is because of

some other non-agricultural measures (might be

industrial sector, household emission production, or

neighboring countries producing a harmful effect on

agriculture productivity).

4.4 Results of Climatic Vulnerability and

Farmers' Perception

The results of multinomial logistic regression are

given in Table 5 (Appendices). The strongly

disagree response is considered as a reference

category about the farmer's perception of climatic

vulnerability in agriculture output. Where, the

dependent variable is agriculture's vulnerability to

climate change, while independent variables are Per

Hectare Yields, Soil Fertility, Rise in Temperature,

Crop Varieties, Night Temperature, Harvesting

Time, Women Farmers, Extension Services, and

Small Farmer's Vulnerability. The estimates show

that most results are significant at 10, 5, and 1

percent levels of significance. The probability

values of estimated coefficients are consistent with

our expected hypothesis. The regression coefficient

values are in Table 5 (Appendices), which represent

the ordered multinomial logit model, the odds

coefficient, and odd ratios.

(i) Strongly Agree Estimates

The slope coefficient of the variable ‘per hectare

yield’ is -2.20, which is statistically significant. If

the per hectare yield increases, the agriculture

climatic vulnerability will reduce by (2.20) and vice

versa. The estimated results indicate that ‘per

hectare yield’ is a factor that can overcome the

farmer’s climatic vulnerability. Higher crop yield is

the main instrument to identify the farmer’s

perception and highlights the climatic damages by

keeping the other productivity instruments constant,

[14], [57], [58]. The farmer’s perception of CC is

reflected by a change in per-hectare yield. If the

climate is pleasant, the agriculture per hectare yield

will increase, while rapidly changing weather is

problematic for agricultural output. The estimated

results are inconsistent with the findings of a

research, [36], and consistent with the results of

other study, [41]. As, [36], concluded that climate

change is reducing agriculture productivity over

time because of its harsh impact on per-hectare

yield. The slope coefficient of the variable ‘increase

in temperature’ is negative (-2.73) and statistically

significant. The farmers strongly agreed that an

increase in temperature is a primary factor for

agricultural crops, and crop yield is dependent on

favorable temperature. As the temperature increases,

the farmer’s vulnerability to climate change will

reduce by 2.73 units. The increase in temperature

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has a varied effect on different crops depending on

the location, crop varieties, and categories (like

wheat, rice, cotton, etc.). Consistently, an increase

in temperature and duration does not mean that crop

yields and output will reduce, [36], [57], [58]. The

findings are in contradiction with the outcome of a

research, [21], which concluded that soil moisture is

affected due to an increase in night temperature,

which badly affects the growth of crops and

productivity.

The change in harvesting time is a challenging

issue for the farming community because the

harvesting time affects the sowing pattern of the

subsequent crops. The coefficient value of the

harvesting time variable has a statistically

significant and negative impact on agricultural

vulnerability to climate change. The coefficient

value is -1.8 with a probability value of 0.060.

Change in harvesting time affects the whole sowing

and harvesting circle, which causes less agricultural

productivity. The findings are consistent with the

outcome of, [31], [36], [41]. The coefficient value of

crop varieties has an insignificant impact on

agriculture vulnerability. The reason is that the

agricultural crop varieties are not climate-reliant to

protect output and grow smoothly in varying/harsh

environments, [9], [25]. The estimated coefficient of

extension services is significant and negative. The

contribution of the extension department in the

provision of awareness about climatic vulnerability

to farmers is negative. This shows that the extension

department is not performing a productive role in

educating and guiding the farmers about the climatic

challenges. These outcomes are consistent with a

study, [3], in which it was concluded that the

government must focus on running a policy of

extension services and implement it to facilitate the

farmers regarding early adaptation measures to

climate changes, [3], [4].

The empirical results of the variable

‘vulnerability of women farmers’ have a positive

and insignificant impact on agriculture's

vulnerability to climate change. Women farmers are

not highly vulnerable to climate change due to their

limited role in managerial activity and in decision-

making in sowing and harvesting activities, [24],

[68], [69]. The slope coefficient value of ‘small

farmer’s vulnerability is highly significant and

negative impact on agriculture vulnerability to CC.

The coefficient value of a small farmer’s

vulnerability is -1.3, which is significant at 0.07

percent. In Pakistan, agricultural land is not

uniformly distributed, and small farmers hold a

larger share of cultivated land, [7], [24]. The

fundamental reason for a small landholder’s

vulnerability to CC is a lack of information and

resources for adaptive measures. The estimated

coefficient of the soil fertility variable is negative

but insignificant. Soil fertility does not provide any

impact on agriculture's vulnerability to CC, [24].

(ii) Agree Estimates

In the case of agreed estimates, the significance

level of slope coefficients of all independent

variables is consistent with the results of strongly

agreed estimates. However, there is a small

variation in the magnitude of slope coefficients in

both categories of estimates. The consistency in

estimated results of ‘strongly agree’ and ‘agree’

responses is a validation of analysis and reflects the

true policy message for stakeholders. The

coefficient value of ‘per hectare yield’ is -2.06,

which is statistically significant and follows the

outcome of the strongly agreed coefficient.

Similarly, the agreed slope coefficient of variables

‘increase in temperature’, ‘harvesting time’, ‘crop

varieties’, ‘extension services’, ‘vulnerability of

women farmers’, and ‘small farmer’s vulnerability’

are consistent with the estimated results of strongly

agreed estimates. The results are justified and

consistent with the outcomes of some other studies,

[4], [9], [25], [68].

(iii) Neutral Estimates

The neutral coefficients show that only one variable

has a significant impact on the agriculture climatic

vulnerability. This indicates that the farmers’

perception of climatic vulnerability is clear, and

farmers’ response is realistic about the

environmental factors. The results of independent

variables under neutral response are not significant

and the explanatory variables show either agreed

response or disagreed impact on climatic

vulnerability and agriculture productivity. The slope

coefficient of per hectare yield is negative and

statistically significant, which is consistent with the

outcome of strongly agree. The farmers are not

considering that climate change is not the only

indicator for a reduction in per hectare yield as there

are a lot of other pitiful factors affecting agriculture

productivity. Further, the list of other variables

shows the insignificant impact on agriculture's

vulnerability to CC.

(iv) Disagree Estimates

In the case of a disagreeing response, the level of

significance has a contradiction with strongly agreed

choices. Most variables are insignificant except four

variables, such as ‘per hectare yields’, ‘harvesting

time’, ‘extension services’, and ‘crop varieties’. The

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other variables have contradictory responses.

Additionally, the farmers strongly agreed and have a

consistent behavior under the regressive analysis.

The crop varieties variable shows an opposite

response compared to the strongly agreed response,

the crop variety variable has a significant and

positive impact on agriculture's vulnerability to

climate change. This indicated that farmers

disagreed about the importance of innovative seed

varieties which are climatic resilient and pest

control. The extension services do not provide

knowledge about innovative seeds and farmers use

their traditional seeds for future sowing process. It

concluded that farmers do not have access and

resources for early adoption of innovative crop

varieties, [56], [59].

The coefficient value of the variable per hectare

yield is -1.9, which is significant and consistent with

the strongly agreed coefficient. The per hectare

yield depends on a couple of factors like water

availability, crop variety, fertilizers, pesticides, soil

fertility, farmer’s working ability, agricultural

credit, climate change, pleasant weather, etc., [1],

[41]. The farmers are significantly disagreeing with

the statement of changing of harvesting pattern. The

coefficient value of the variable of harvesting time

is consistent with a strongly agreed coefficient with

high magnitude. Besides, the coefficient value of

crop varieties has a positive and significant impact

on agriculture vulnerability to CC. The magnitude

of the slope coefficient of crop varieties is -2.40,

which is higher than the ‘strongly agreed’

coefficient. The extension services coefficient has a

highly significant (0.01) impact on farmers’

satisfaction with climatic resilient institutions. The

coefficient value of extension services is

significantly positive, which indicates that farmers

are not satisfied with the provision of suitable

extension services. The contribution of the

extension department in knowledge provision about

climatic challenges in agriculture production is

negative.

4.5 Model Processing Summary and

Goodness of Fit

The model processing summary results are given in

Table 3 (Appendices), showing that 33.9 percent of

farmers responded neutrally, which indicates

agrarians are not well know about the functioning of

the climate change institute in Punjab. Despite this,

38 percent of respondents are not satisfied with the

performance of climate change institutes. The

findings are in line with the outcomes of some other

studies, [4], [25], [26], [36]. 28 percent of

respondents are satisfied with the performance,

working pattern, and facilitation of climate-resilient

institutes in Punjab. The farmer's satisfaction with

the resilient institute is captured through

multinomial regression analysis, and model-fitted

estimates are given in Table 4 (Appendices). The

empirical results in the goodness of fit of the given

model show the legitimacy of the estimated

coefficient. The estimated value of the Chi-Square

of likelihood measure is high (22.72) with zero

probability value, which rejects the null hypothesis

significantly. So, the estimated model is well-fitted,

and estimates are good for policy purposes. The

value of Chi-Square is high, which shows the

independent variables have a strong influence on the

probability of agriculture vulnerability to CC.

Similarly, the estimated value of Pearson is highly

significant (at 0.00 probability value) and Deviance

is highly insignificant (at 1.00 probability value).

This justifies the goodness of fit of the estimated

model. The results show that the value of the

likelihood ratio test is statistically significant (at

0.00) and high (222.72), so the model provides

fruitful results for the policy aspect.

(i) Strongly Satisfied Estimates

For MLR analysis, the predicted variable is the

climate-resilient institutions in Punjab, while

independent variables are related to farmers’

satisfaction level regarding CC policies and

institutional setup. The independent variables are

Laws and Regulations, Weather Mechanisms,

NGOs, International Organizations, Community

interventions, Climate Funds, and Punjab

Government Institutions. The regression coefficient

values are given in Table 6 (Appendices). The

estimated result shows that the slope coefficient of

variable laws and regulations has a negative (-1.30)

and significant (0.002) relationship with climate-

resilient institutional structure in Punjab, Pakistan.

The results indicate that due to the lack of laws and

regulations, the climate change resilient institute

cannot function properly. The negative coefficient

of law and regulation highlights that farmers do not

have information about the functioning and

importance of the climate-resilient institutions in

Punjab. Mere policy development is not the real

achievement; execution of the climate change policy

is highly needed, [25], [28], [46], [54].

The slope coefficient of the variable ‘weather

and disaster alert’ has a significant and positive

impact. The coefficient value is 0.92, which is

significant at 0.002. The estimates show that

weather and disaster alert departments work

proficiently and guide the farmers on time. The

farmers are satisfied with the working procedure of

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weather and disaster alert institutes, [1], [9], [26].

The climate change structural development is

independent of the role of NGOs, which highlights

that there are not sufficient NGOs working on

climate change information distribution in Punjab.

The estimated outcomes are consistent with the

findings of [24], [46], who argued that the NGOs

working on climate change have not diffused the

information to farmers on time. They have a very

limited and unproductive role in information

disbursement to the farmers in Punjab, Pakistan.

The coefficient value of the variable

‘international organizations’ is negative (-0.73) and

statistically significant. This means that the

Farmers’ satisfaction level with international

organizations is negative. The international

organizations collect the climate funds for

institutional development in Pakistan, but the funds

are not utilized for the structural development of

institutes, [6], [24]. Results are consistent with the

findings of a few studies, [70], [71]. The estimated

coefficient of climate-related funds and community-

level intervention shows an insignificant impact on

institutional structure in Punjab. The results show

that climate change funds are not utilized to

incentivize the farmers and institutional

development in Punjab, [4], [46]. The farmers can

attain more benefits from institutional setup and

easy access to climatic information to increase

agriculture productivity. The coefficient value of the

variable Punjab government climatic response is

negative and significant. This shows that the Punjab

government has a negative impact on institutional

development. The government policy regarding

farmers' awareness and education about climatic

challenges is farmers in Punjab, Pakistan. But in

reality, farmers are not satisfied with the Punjab

government's actions on climatic change knowledge

disbursement among the farmers, [33].

(ii) Satisfied Estimates

In the case of satisfied estimates, most of the

variables have similar outcomes with strongly

satisfied estimates. The empirical estimate shows

that the slope coefficient of variable laws and

regulations is consistent with strongly satisfied

estimates. The role of climate change resilient

institutes is weak because of poor climate change

laws and their implementation. The coefficient

value of the variable ‘weather and disaster alert’ is

statistically significant and positive, whereas the

outcomes are consistent with results of strongly

satisfied. The farmers are satisfied with the working

procedure of weather and disaster alert institutes,

[1], [9], [26], [70]. The coefficient values of NGOs

have no significant impact on the institutional setup

and development of climate change-resilient

institutes in Punjab. The slope coefficient of the

variable international organizations is negative and

statistically significant. The agrarian's satisfaction

level with international organizations is negative, so

the results are consistent with strongly satisfied

results. The outcomes are in line with the findings of

some studies, [23], [46], in which it has been argued

that the international organizations on CC have not

diffused the information to farmers on time and

have a very limited and unproductive role in

information disbursement to the farmers in Punjab,

Pakistan. Consistently, the estimated coefficient of

climate-related funds and community-level

intervention shows an insignificant impact on

institutional structure in Punjab. Further, the

estimates of the Punjab government's response to

climatic changes are negative and significant, which

emphasizes that government policies are just papers

and are not working at the field level to facilitate the

farmers about climatic challenges, [70].

(iii) Neutral Estimates

In the case of neutral responses, the estimated

coefficient has a consistent outcome with strongly

satisfied results, except ‘community interaction’ and

‘climatic funds’ variables. The results of neutral

choices show that the slope coefficient of the

variable ‘laws and regulations’ has a negative (-

0.80) and significant (0.006) relationship with

climate-resilient institutional structure in Punjab,

The results indicate that the lack of laws and

regulations, the climate change resilient institute

cannot functioning properly. The negative

coefficient of law and regulation highlights that

farmers do not have information about the

functioning and importance of climate-resilient

institutions in the province. The policy development

should not be considered as an achievement;

execution of the climate change policy is highly

needed, [24], [46]. The coefficient value of

‘community-level’ interaction is positive and

significant, so community-level interactions have a

neutral response in the development of climate-

resilient institutes in Punjab. This indicated that at

the community level, people do not have

information about climatic institutions and their

functional role for the farmers, [9], [28]. Similarly,

the coefficient results of climate-related funds are

inconsistent with strongly satisfied results and show

a significant impact in climate-resilient institutes in

Punjab. The results show that climate change funds

are not working for institutional development in

Punjab even farmers are not aware of climatic

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funds. The coveted outcomes of climate fund

utilization are not being attained because of poor

management and misuse of available funds.

(iv) Dissatisfied Estimates

In the case of dissatisfied estimates, one variable is

significant (Punjab Government response), while all

other variables are insignificant and have

inconsistent results with strongly satisfied results.

The coefficient value of the ‘Punjab government’

has a positive and significant impact on climate-

resilient institutional setup. This indicates that

farmers are dissatisfied with the functioning of

government climate institutions in Punjab. The

results are consistent with the outcomes of [33],

[70], who argued that the government has a poor

institutional structure for the framers to provide

knowledge about climate vulnerability. The

insignificant behavior of other indicators shows that

all the variables have consistency in a dependency

relationship with the climatic institutional setup in

Punjab. This highlights that the results address the

policy message, and there is no contradiction in

estimated outcomes.

5 Conclusion and Policy

Recommendations

Broadly speaking, climate vulnerability increases

agriculture uncertainty, which ultimately reduces

agriculture productivity. Temperature variation,

changing patterns in precipitation, mutable sowing,

and harvesting time create an alarming situation for

agriculture productivity in the province. Based on

the results of multinomial logistic regression, it is

concluded that the farmers’ perception of climate

change is dependent on per-hectare yield. As the per

hectare yield increases the farmer's vulnerability to

climate change declines over time. The farmers

strongly agreed that increasing temperature is

destroying the sowing and harvesting pattern of the

crop as the favorable temperature is highly essential

for agricultural productivity. The farmer’s

perception of night temperature is positive. They

concluded that night temperature affects the soil

moisture. The coefficient value of the variable

‘harvesting and sowing time’ has a negative impact

on farmer’s climatic vulnerability. The farmers held

the opinion that changing the sowing and harvesting

cycle caused the agriculture productivity negatively.

The coefficient value of crop varieties has an

insignificant impact on agriculture climatic

vulnerability. This shows that the agricultural crop

varieties are not climate resistant to protect output

and grow smoothly in varying/harsh environments.

The farmers’ perception of the role of extension

services is negative. The extension department

failed to educate the farmers, disseminate the

climate information, and guide the farmers about the

vulnerable effects of upcoming weather. The results

indicate that women farmers are vulnerable to

climate change. This is so because women farmers

have limited roles in managerial activity and

decision-making in cultivation. The small farmer’s

perception of adaptive measures of climatic

challenges is negative because the small farmers

with small landholders lack the resources and

information about climatic challenges.

It is concluded that the farmers are not satisfied with

the functioning of climate-resilient institutions.

They are not satisfied with the ‘laws and

regulations’ of climate institutions. They do not

have information about the functioning and

importance of climatic resilient institutions in

Punjab. They are not satisfied (even unaware) with

government and climate-resilient institutional

structure and their responsibility to educate the

farmer about vulnerable climate. Farmers are

dissatisfied with the performance and functioning of

research institutions and NGOs in Punjab. The

coefficient values of NGOs have a negative and

insignificant impact on institutional setup and

development of climate change resilient institutes in

Punjab in case of neutral response. The results show

that climate change funds are not utilized for climate

institutional development in Punjab. Another

coefficient value shows that the Punjab government

has a negative impact on institutional structure

building. Following policy recommendations may

be taken up with an appropriate effective

implementation plan:

1. The government and research institutions

should focus on the development of crop

varieties to be drought-resistant, heat-

resistant, and absorb climate shocks.

2. It is a challenge for policy experts, research

institutes, and NGOs to predict new sowing

and harvesting patterns to avoid detrimental

CC in the agriculture sector of Punjab,

Pakistan.

3. The extension department should educate

the farmers, about the sowing and

harvesting pattern of crops that could help

in increasing productivity.

4. Government should develop the

coordination between climate-resilient

institutions and agrarians to introduce new

climate zones, through which farmers can

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adopt alternative crops according to a

particular climate.

5. The performance of public-private

partnerships may be helpful to protect

climate vulnerability.

6. The government should focus on the

appropriate allocation of climate funds and

their utilization through public-private

partnerships.

7. The government can also increase the

adaptation measures through a suitable

credit policy for the farmers in Punjab

Pakistan.

6 Limitation and Way forward

This research has been pursued with maximum

efforts within the stipulated time period. Due to

financial constraints, the sample size could not be

widely extended to the highest optimal level. Lack

of education and ignorance on the part of many of

the respondents, the important information could not

be retrieved. The variation across District and

Tehsil levels might not be fully addressed. The

main focus was on male farmers. In the future, it

would be more appropriate to follow a gender-

sensitive approach in such field surveys by focusing

on the exclusive impact of climate change on rural

women. Similarly, in future climate studies, all the

quantitative analysis should be testified through the

prism of opinion and perception of all key

stakeholders, particularly the farming community.

Some minute issues may also be highlighted if

Focus Group Discussions (FGDs) and in-depth

interviews are arranged with the farmers. This is

how the research gaps that remained unfilled in

terms of sample errors and structural issues of time

series data may be addressed reasonably.

Acknowledgment:

Dr. Touqeer Ahmad and Dr. Khawar Hassan are

highly acknowledged for their fieldwork in the

collection of primary data.

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APPENDICES

Table 1. Study districts of Punjab, Pakistan

Attock

Bahawalnagar

Bahawalpur

Bhakkar

Chakwal

Chiniot

D.G.Khan

Faisalabad

Gujranwala

Gujrat

Hafizabad

Jhang

Jhelum

Kasur

Khanewal

Khushab

Lahore

Layyah

Lodhraan

Mandi Bahuddin

Mianwali

Multan

Muzaffargarh

Nankana Sahib

Narowal

Sahiwal

Okara

Pakpattan

Rahim Yar Khan

Rajanpur

Rawalpindi

Sargodha

Sheikhupura

Sialkot

T.T.Singh

Vehari

Table 2. Summary statistics

Sr. No

Variables

N

Mean

Minimum

Maximum

Std. Deviation

D1

Respondent Age

351

44.37

22.0

75.0

9.96

D2

Acres of land holding

360

18.42

0.0

147.0

19.99

P1

Vulnerability to CC

360

1.78

1.0

5.0

0.85

P2

Per Hectares Yield

360

2.180

1.0

5.0

0.98

P3

Increase in temperature

360

1.76

1.0

5.0

0.69

P4

Lack of precipitation

360

1.79

1.0

4.0

0.76

P5

Night temperature

360

2.36

1.0

5.0

0.97

P6

Sowing Time

360

2.18

1.0

5.0

0.94

P7

Harvesting Time

360

2.23

1.0

5.0

0.90

P8

Crop Varieties

360

2.42

1.0

5.0

0.92

P9

Extension Services

360

2.80

1.0

5.0

0.99

P10

Women Farmer

360

2.75

1.0

5.0

1.00

P11

Adaptation measures

360

2.82

1.0

5.0

1.14

P12

Small Farmers Vulnerability

360

2.16

1.0

5.0

1.10

P13

Extreme Events

360

3.37

1.0

5.0

1.21

P14

Soil Fertility

360

3.51

1.0

5.0

1.21

P15

Farming Migration

360

2.54

1.0

5.0

1.14

S1

Punjab Government

360

2.95

1.0

5.0

1.12

S2

Institutional Structure

360

3.14

1.0

5.0

1.02

S3

Laws & Regulation

360

3.32

1.0

5.0

1.11

S4

Research Institutes

360

3.18

1.0

5.0

1.13

S5

Weather Mechanism

360

2.81

1.0

5.0

1.23

S6

NGOs

360

3.26

1.0

5.0

1.02

S7

International Organizations

360

3.18

1.0

5.0

1.10

S8

Community Interventions

360

3.01

1.0

5.0

0.99

S9

Climate Funds

360

3.46

1.0

5.0

1.14

S10

Public Private Partnership (PPP)

360

3.35

1.0

5.0

1.07

Where D’s represent the demographic variables, P’s represent the variables about the farmer’s perception regarding

CC, and S’s are used for farmer satisfaction level about policy adoption related to CC.

Respondent age was measured in years, while the land holding of the farmers was measured in the number of acres.

The variables representing the farmer’s perception of climate change were measured on a Likert Scale of strongly agree

to strongly disagree with the range starting from 1 to 5. The variables related to the farmer's satisfaction with climatic

resilient institutions and policies were also measured on a Likert scale of strongly satisfied to strongly dissatisfied with

the range starting from 1 to 5.

Source: Author’s Calculation.

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Table 3. Model processing summary

Agriculture's Vulnerability to

Climate Change

Farmer's Satisfaction with Climate Resilient Institutes

Scale

N

Marginal

Percentage

N

Marginal Percentage

Strongly Agree

150

41.7

15

4.3

Agree

163

45.3

84

23.9

Neutral

28

7.8

119

33.9

Disagree

14

3.9

103

29.3

Strongly Disagree

5

1.4

30

8.5

Valid

360

100.0

351

100.0

Source: Author’s Calculation.

Table 4. Model fitted information

Test

Agriculture's Vulnerability to

Climate Change

Farmer's Satisfaction with Climate

Resilient Institutes

Likelihood Ratio Tests

Chi-Square

119.526

(0.000)

222.724

(0.000)

Goodness-of-Fit

Pearson

1414.236

(0.036)

1424.45

(0.000)

Deviance

667.713

(1.000)

837.43

(1.000)

Pseudo R-Square

Nagelkerke

0.317

0.499

Cox and Snell

0.283

0.470

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Table 5. Estimated coefficient of agriculture vulnerability to climate change in Punjab

Scale

Variables

Coefficient

Std. Error

Wald

Sig.

Odd ratios

S.A

Intercept

25.05

9.37

7.14

0.01

Per Hectares Yield

-2.199

0.746

8.688

0.003

0.111

Increase in temperature

-2.732

1.254

4.744

0.029

0.065

Night temperature

-0.261

.621

.177

0.674

0.770

Harvesting Time

-1.800

0.957

3.535

0.060

0.165

Crop Varieties

0.928

0.909

1.042

0.307

2.529

Extension Services

-1.486

0.902

2.718

0.099

0.226

Women Farmer

1.106

0.834

1.758

0.185

3.023

Small Farmers Vulnerability

-1.369

0.757

3.276

0.070

0.254

Soil Fertility

-0.195

0.631

0.096

0.757

0.823

A

Intercept

25.114

9.362

7.196

0.007

Per Hectares Yield

-2.065

0.745

7.686

0.006

0.127

Increase in temperature

-2.366

1.248

3.595

0.058

0.094

Night temperature

-0.033

0.617

0.003

0.957

0.968

Harvesting Time

-1.861

0.955

3.802

0.051

0.155

Crop Varieties

0.888

0.907

0.957

0.328

2.430

Extension Services

-1.606

0.900

3.185

0.074

0.201

Women Farmer

0.790

0.832

0.903

0.342

2.203

Small Farmers Vulnerability

-1.238

0.754

2.695

0.100

0.290

Soil Fertility

-0.409

0.628

0.425

0.515

0.664

N

Intercept

20.122

9.412

4.571

0.003

Per Hectares Yield

-1.720

0.757

5.160

0.023

0.179

Increase in temperature

-1.953

1.266

2.381

0.123

0.142

Night temperature

-0.235

0.645

0.133

0.715

0.790

Harvesting Time

-1.462

0.974

2.252

0.133

0.232

Crop Varieties

1.096

0.925

1.405

0.236

2.993

Extension Services

-1.238

0.912

1.841

0.175

0.290

Women Farmer

0.611

0.848

0.519

0.471

1.842

Small Farmers Vulnerability

-1.129

0.766

2.172

0.141

0.323

Soil Fertility

-0.479

0.639

0.561

0.454

0.619

D.A

Intercept

16.332

9.421

3.005

0.083

Per Hectares Yield

-1.910

0.779

6.011

0.014

0.148

Increase in temperature

-1.442

1.298

1.234

0.267

0.236

Night temperature

0.100

0.666

0.022

0.881

1.105

Harvesting Time

-2.094

1.004

4.347

0.037

0.123

Crop Varieties

1.710

0.954

3.217

0.073

5.531

Extension Services

-2.403

0.964

6.207

0.013

0.090

Women Farmer

1.104

0.873

1.600

0.206

3.017

Small Farmers Vulnerability

-.541

0.784

0.476

0.490

0.582

Soil Fertility

-.404

0.670

0.363

0.547

0.668

Whereas S.A is Strongly Agree, A is Agree, N is Neutral and D.A is Disagree

Source: Author’s Calculations

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Table 6. Results of farmer’s satisfaction about climatic institutional development

Variables

Coefficient

Std. Error

Wald

Sig.

Odds

S.S

Intercept

15.30

2.93

27.19

0.00

Laws & Regulation

-1.304

0.421

9.595

0.002

0.272

Weather Mechanism

0.920

0.301

9.307

0.002

2.508

NGOs

-0.441

0.418

1.113

0.291

0.643

International Organizations

-0.724

0.426

2.890

0.089

0.485

Community Interventions

0.266

0.425

0.390

0.532

1.304

Climate Funds

-0.564

0.412

1.869

0.172

0.569

Punjab Government

-2.502

0.536

21.825

0.000

0.082

S

Intercept

12.243

2.286

28.676

0.000

Laws & Regulation

-0.913

0.306

8.881

0.003

0.401

Weather Mechanism

0.503

0.239

4.433

0.035

1.653

NGOs

-0.456

0.307

2.213

0.137

0.634

International Organizations

-0.554

0.320

2.997

0.083

0.574

Community Interventions

0.199

0.281

0.499

0.480

1.220

Climate Funds

-0.395

0.280

1.989

0.158

0.673

Punjab Government

-1.579

0.292

29.181

0.000

0.206

N

Intercept

8.491

2.208

14.787

0.000

Laws & Regulation

-0.807

0.291

7.674

0.006

0.446

Weather Mechanism

0.578

0.219

6.994

0.008

1.783

NGOs

-0.516

0.294

3.076

0.079

0.597

International Organizations

-0.177

0.304

0.337

0.562

0.838

Community Interventions

0.555

0.260

4.551

0.033

1.742

Climate Funds

-0.229

0.267

0.740

0.390

.795

Punjab Government

-1.258

0.268

22.033

0.000

0.284

D.S

Intercept

3.512

2.105

2.784

0.095

Laws & Regulation

-0.156

0.280

0.310

0.578

0.856

Weather Mechanism

0.190

0.202

0.890

0.345

1.209

NGOs

-.338

0.282

1.436

0.231

0.713

International Organizations

0.038

0.293

0.017

0.897

1.039

Community Interventions

0.200

0.237

0.716

0.398

1.222

Climate Funds

-0.020

0.254

0.006

0.937

0.980

Punjab Government

-0.518

0.247

4.378

0.036

0.596

Here S.S is Strongly Satisfied, S is Satisfied, N is Neutral and D.S is Dissatisfied

Source: Author’s Calculation.

WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2023.19.102

Zeeshan Shabbir Rana, Intizar Hussain,

Abdul Saboor, Muhammad Usman,

Shumaila Sadiq, Nasir Mahmood, Lal Khan Almas

E-ISSN: 2224-3496

1101

Volume 19, 2023

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

This is the part of the research project in which

Z.S.R. pursued his M.Sc. (Climate Change) at

University College London (UCL), UK. I. H.

diligently articulated some of the fundamental ideas

in the synopsis of this paper. A.S. pursued quite

carefully all the literature review and questionnaire

development for this research project. M.U. worked

on data collection, data compilation, data analysis,

write-up of methodology, results, and discussion

and conclusion. L.K.A, M.U., and N. M worked on

the reviewer’s comments incorporation and revised

the whole manuscript in the light of the reviewer’s

comments. S.S. took great care of the field affairs in

data collection and methodological considerations.

N.M. and L.K.A reviewed the manuscript and

worked as corresponding authors.

Consent to Participate:

The respondents were briefed about the research

purpose and then the interview was conducted upon

their full willingness to provide the requested

information.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

The publication cost of this manuscript has been

supported by the USDA-ARS Ogallala Aquifer

Program. The visiting Research Fellow from

Pakistan had been funded by the Punjab Higher

Education Commission (PHEC), Pakistan through

his institution, PMAS- Arid Agriculture University,

Rawalpindi, Pakistan.

Conflicts of Interest

The authors declare no conflict of interest.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2023.19.102

Zeeshan Shabbir Rana, Intizar Hussain,

Abdul Saboor, Muhammad Usman,

Shumaila Sadiq, Nasir Mahmood, Lal Khan Almas

E-ISSN: 2224-3496

1102

Volume 19, 2023