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# Adaptation to Climate Change in Rain-Fed Farming System in Punjab, Pakistan

## Abstract

Keywords:
How to Cite: Bakhsh, K., & Kamran, M. A. (2019). Adaptation to Climate Change in Rain-Fed Farming System in Punjab, Pakistan. International Journal of the Commons, 13(2), 833–847. DOI: http://doi.org/10.5334/ijc.887
Published on 30 Oct 2019
Accepted on 13 Sep 2019            Submitted on 12 Apr 2018

## 1. Introduction

Climate change has significant impacts on agriculture (Lobell et al., 2011) and has potential to further impact it through changing rainfall pattern, drought, floods, increase in average high temperature, etc. The negative effects of these changes are expected to be more common than positive effects. The intensity of negative effects on agriculture (Mendelsohn et al., 2006; Kok et al., 2016) and the poor (Schmidhuber and Tubiello, 2007) will be higher in developing countries due to high vulnerability and poor economic and technical capacity to respond the menace (Padgham, 2009). It is threatening small farmers’ ability to remain in business in shifting conditions with poor resource base to adapt. This also has implications to increase poor and rich divide.

It is imperative to mention that climate change has detrimental effect on agriculture in the absence of adaptation strategies. However, a reduction in the effects is partly possible by adapting various measures at the farm level (Downing, 1991; Easterling III et al., 1993; Reilly and Schimmelpfennig, 1999; Smit and Skinner, 2002; Ali and Erenstein, 2017; Grost et al., 2018; Cholo et al., 2019). Gbetibouo (2009) argues that the adaptive capacity of the farmers is equally important in reducing the impact of climate change on agriculture. Soil biodiversity as adaptation to climate change (Pascual et al., 2015) contributes in increasing and stabilizing agriculture productivity in the rain-fed farming systems (Sidibé et al., 2018). However, inadequate technical knowledge, low financial resources and small farm size are important challenges in agricultural adaptation (Kichamu et al., 2018).

The literature on climate change adaptation has focused on quantification of impacts (Hansen et al., 2006; Stern, 2006) and assessment of the vulnerability of communities and ecosystems (Turner et al., 2003; Adger et al., 2007), division of adaptation efforts into structural, physical, institutional categories (Bastakoti et al., 2014), and identification of obstacles to adaptation (Burch, 2010).

### 1.1. Climate change adaptation in agriculture in Pakistan

Like other developing countries, the agriculture sector of Pakistan is highly vulnerable to climate change impacts. Many studies indicate that rising temperature is associated with a decline in agriculture productivity (Sultana and Ali, 2006; Aggarwal and Sivakumar, 2011; Mahmood et al., 2012; Ahmad et al., 2013; Tariq et al., 2014; Gorst et al., 2018). Siddiqui et al. (2012) estimated significant and negative impacts on widely-grown staple crops namely rice and wheat. These impacts on staple crops threaten food security in Pakistan because population heavily depends on wheat and rice for meeting food requirements.

Adaptation to climate change in agriculture is considered important for reducing the negative effects on the agriculture productivity. However, adaptation is not enough high in Pakistan mainly because of less knowledge of climate change (Abid et al., 2015, 2016; Rauf et al., 2017). Stocker et al. (2013) attributed the severe impacts of climate change on agriculture to poor infrastructure and low adaptive capacity in Pakistan. Literature on adaptation in agriculture in Pakistan is limited and only a few studies are available analyzing adaptation to climate change (Abid et al., 2015, 2016; Esham and Garforth, 2013; Ali and Erenstein, 2017; Gorst et al., 2018). Some important adaptation strategies reported in the literature on Pakistan include changing planting dates, crop varieties (Abid et al., 2016; Ali and Erenstein, 2017), fertilizer types, planting trees (Abid et al., 2015, 2016, 2016a) and shifting to new crops (Ali and Erenstein, 2017).

Socioeconomic factors of farm and farmers are considered critical in agricultural adaptation to climate change. Education (Abid et al., 2016; Ali and Erenstein, 2017), farming experience (Abid et al., 2015, 2016), access to agricultural extension and credit (Abid et al., 2016; Ali and Erenstein, 2017; Gorst et al., 2018), institutional and informational constraints (Gorst et al., 2018), farm size and household size (Ali and Erenstein, 2017) are significant factors related with adaptation to climate change. Policies focusing on these factors can lead to an increase in agriculture adaptation in order to reduce risk in agriculture. This in effect is associated with food security of Pakistan.

### 1.2. Objectives and contribution of the study

It is equally important to understand local adaptation measures and constraints and understand nature of adaptation efforts at private and government level. The provision of adaptation goods is categorized into public or private (Tompkins and Eakin, 2012). The nature of adaptation goods provided by private and public institutions can be of public or private and that gives rise to free riding and under-provision. Similarly the non-targeted subsidies with benefits to small segment of well-off individuals may result in wastage of resources and trigger inequality.

The government’s ability to support the farmers is limited due to resource constraints and extent of the issues in developing countries. The governments in developing countries have urban bias in public investment to support ever increasing urban population and industrial activities. The problem is further aggravated as major part of agricultural investment is allocated for irrigated agriculture due to high expected returns. This has left the rain-fed and other marginal farming with productivity deprived of public support (Zia et al., 1997). The water management, moisture conservation and nutritional management are major agricultural constraints of rain-fed farming and that puts higher reliance on natural forces. It is therefore pertinent to note that variation in rainfall pattern and heat stress due to climate change can heavily hit these areas. In contrast, irrigated agriculture has natural advantage to cope climatic variability.

Wani et al. (2009) find that globally 80% of the agricultural land area is rain-fed which generates 65% to 70% staple foods but 70% of the population inhabiting in these areas are poor due to low and variable productivity. About 69% of all cereal area is rain-fed, including 40% of rice, 66% of wheat, 82% of maize and 86% of other coarse grains (Rosegrant et al., 2002). Rain-fed agriculture can increase net returns per hectare substantially through crop improvement and natural resource management interventions (Harrisk and Orr, 2014). Siderius et al. (2016) argue that the rain-fed area of the Nile has the potential to meet above 75% of the needed increase in food production by 2025. This implies the importance of the rain-fed agriculture to meet the rising demand for food production in the future.

## 2. Data and Source

### 2.1. Study area

The Punjab province being the largest province in term of population is also the major contributor in agricultural production in Pakistan. Further, Punjab province is divided into rain-fed and irrigated Punjab on the basis of mode of irrigation. The northern part of Punjab province (aka Pothowar plateau) is comprised of rain-fed farming system (Figure 1). The climate of this region is predominantly semi-arid or barani. Rawalpindi division includes all rain-fed districts of the region, receiving annual rainfall below 1000 mm from 2002 to 2016 with the exception of a few years (Table 1). The rainfall follows erratic pattern with most of the rainfall (about 70%) in months of June-September i.e. summer season while the winter season gets long spells of gentle showers. Considering temperature, mean of minimum and maximum temperature shows an increasing trend during the period of 2002 to 2016 (Table 1). Further, the area has generally slightly undulating slopes with low hill ranges. Arid region of the province is exposed to adverse effects of climate change.

Figure 1

Map of the study area.

Table 1

Annual rainfall and temperature data for 2002–2016.

Year Maximum Temperature (°C) Rainfall (mm)

2002 30 931
2003 29 1247
2004 30 1027
2005 28 973
2006 29 1598
2007 29 1828
2008 29 1388
2009 30 607
2010 30 1018
2011 29 1018
2012 29 1023
2013 31 925
2014 28 1507
2015 28 1621
2016 30 1044

Source: Govt of Punjab (2016).

Chakwal district was selected from the province as agriculture production in the district predominantly depends on rainfall. Two cropping seasons namely rabi (October–April) and kharif (May–September) seasons are traditionally followed keeping in consideration the rainfall probabilities and temperature for different crop growth stages. Wheat and peanuts are the important crops planted in the study area. This district is the most important area (occupies 27% of Rawalpindi division) for wheat production among rain-fed farming area of the Punjab. Table 2 shows the importance of Chakwal district in Rawalpindi division, being the rain-fed division. Out of total cultivated area of Rawalpindi division, share of Chakwal district is amounted 32.19%. Further, this district has 33.29% share in the unirrigated1 or rain-fed area of Rawalpindi division and it is therefore considered the most rain dependent district of the arid zone of the province.

Table 2

Land utilization statistics of Chakwal district (thousand hectares).

Particulars Rawalpindi division Chakwal district

Cultivated area 991 319
Total cropped area 839 260
Unirrigated/rain-fed area 778 259
Kharif area 311 102
Rabi area 528 158
Wheat area 484 130

Source: Govt of Punjab (2017).

### 2.2. Data collection method

A total of 190 farmers were interviewed from Chakwal district. A multi-stage stratified cluster random sampling technique was followed as used by the Pakistan Bureau of Statistics. Within the selected districts, rural union councils were selected randomly. Villages (cluster) were then selected randomly with probability proportion to population. We stratified union councils with large (above 10) and small (below or equal to 10) number of villages and 10 to 15 villages were chosen. Households from the selected villages were stratified into small, medium and large farms based on the landholdings where households with below 12.5 acres were considered small, 12.5–<24 acres as medium and above 24 acres holding as large. A 95% confidence interval for the estimates was used to determine the appropriate sample size. This sampling approach produced a sample size that was representative for the rain-fed division of the province for understanding adaptation to climate change in the rain-fed agriculture with the objective of informing policy makers.

A semi-structured, pre-tested questionnaire was used to gather information. The questionnaire included a number of closed-ended and open-ended questions on socioeconomic characteristics, adaptation to climate change, farm assets, etc. In addition, the respondents were asked to provide qualitative information on farmers’ decision to carry activities or to invest in adaptation to climate change in response to the perceived, actual or potential changes resulting from climate change. This sort of information was sought to understand reasons for carrying out activities or investing in adaptation to climate change. We considered activity or investment as an adaptation strategy if it was taken as a conscious investment to solve climate change related problem. We found mainly six adaptation strategies to climate change recorded by the respondents. These included manure application, deep plowing, bund-making, income diversification, crop diversification and land renting-out. Services of four postgraduate students from rural background with primary degree in agricultural economics discipline were hired to collect data during 2013. This helped to get information in native language and translate local language and terminologies.

## 3. Empirical models

A number of farm, farmer and socio-economic characteristics affect the decision on adaptation to climate change. Gender, age, education and experience are important among the farmer characteristics (Knowler and Bradshaw, 2007; Vitale et al., 2011; Baumgart-Getz et al., 2012). D’Emden et al. (2008) and Gedikoglu and McCann (2012) find that farm size, location, proximity of market to house, access to irrigation and the agro-ecological and socio-economic conditions of the area are important determinants of adoption decisions. The economic theory explains that farmers decide to make investment in adaptation to climate change when the expected utility of adaptation $\left({D}_{1}^{*}\right)$ is greater than the utility of non-adaptation $\left({D}_{0}^{*}\right)$. Thus decision on adaptation is observable as a dichotomous choice i.e. Di = 1 if, ${D}_{i}^{*}>{D}_{0}^{*}$ otherwise Di = 0. This can be modeled as:

(1)

where Z is a vector of the explanatory variables, β is a vector of parameters to be estimated and εi is the error term.

Logit model is used to estimate equation 1 as the dependent variable is the dummy of adaptation to climate change i.e. farmers that adapt to climate change and those that don’t adapt to climate change. Six separate logit models for six adaptation practices are estimated using maximum likelihood as these models cannot be consistently estimated using ordinary least square because of the dummy dependent variable in all the six logit models. The logit model is defined as follows:

(2)
$P\left(D=1\right)=\frac{\text{exp}\left({Z}_{i}\beta \right)}{1+exp\left({Z}_{i}\beta \right)}$
(3)
$P\left(D=0\right)=\frac{1}{1+exp\left({Z}_{i}\beta \right)}$

Where D takes the value 1 if the farmer adapts to climate change and 0 otherwise. Z is the row vector of independent variables and β is the corresponding parameter vector to be estimated. Details of dependent variables and explanatory variables used in the above models are presented in Table 3.

Table 3

Characteristics Unit Mean Standard deviation

Socioeconomic characteristics
Age Years 50.55 11.99
Education Schooling years 7.72 3.65
Farming experience Years 21.36 12.93
Family size Numbers 8.21 2.84
Males above 15 years Numbers 2.28 1.14
Females above 15 years Numbers 1.87 0.95
Land area Acres 7.75 15.29
Livestock Animal units 3.02 3.43
Tractor Yes = 1 0.33 0.47
Rotavator Yes = 1 0.04 0.18
Distance from city Km 9.98 7.75
Deep plowing Yes = 1 0.38 0.48
Manure application Yes = 1 0.62 0.49
Bund making Yes = 1 0.54 0.50
Income diversification Yes = 1 0.73 0.44
Crop diversification Yes = 1 0.30 0.46
Land rented out Yes = 1 0.82 0.38

In order to estimate the effect of private adaptation on wheat yield, we employ log-linear production function in the present study. We used different forms of production function, log-linear production function is selected based on signs and significance of the variables and values of R2. Wheat yield in log is considered as dependent variable. All six adaptation practices are taken as explanatory variables. Age, farming experience, family size, males and females above 15 years are also considered as independent variables. However, the results of this regression are interpreted with caution as physical quantities of farm inputs namely fertilizer, seed, labor, etc. are not included due to non-availability of the data. However, variables namely family size, males and females above 15 years are proxy of labor used in wheat production. Deep ploughing used for conserving rainwater and moisture is taken as proxy of irrigation water whereas dummy for manure applied is considered as the representative of fertilizer use.

## 4. Results

### 4.1. Socioeconomic characteristics

Considering manure application and deep plowing as adaptation to climate change, 62% and 38% respondents are found adaptors whereas 54% respondents are found using bund-making as adaptation to climate change. The respondents reporting income diversification are 73% and only 30% farmers are found diversifying crops. The most common adaptation to climate change is land renting out (82%) to fellow farmers (Table 3).

Descriptive statistics in Table 3 show that the respondents are around 51 years old with average education of approximately 8 schooling years. The respondents reported farming experience of 21 years on average, indicating that the respondents have substantial experience in farming and they have learnt better farm practices through experience and observations. Pakistan is among the densely populated countries in the world and the present study depicts that the respondents have large family size i.e. 8 family members and mostly family size comprises children as evident from small number of males and females above 15 years of age. Small farms dominate in the study area as indicated by 7.75 acres land area owned by the respondents. Further, small landholding induces farmers to diversify income so the study finds average 3 livestock heads. Ownership of farm machinery is considered an important asset in adaptation to climate change. We find that 33% farmers possess tractor whereas rotavator is found with 4% farmers only. Distance of the farm from the city is important for having access to information and markets and the mean distance is found to be approximately 10 km that is quiet long distance.

### 4.2. Model results of private adaptation for private benefits

The empirical results obtained from logit models of private adaptation for private benefits are given in Table 4. It is evident from the results of both models that most of the exogenous variables are significantly related with adaptation to climate change i.e. manure application and deep plowing. Livestock is important in explaining the adoption of manure application as more livestock heads will result in higher probability of applying manure at the farm. The farmers having tractor are more likely to apply manure at their farms, since tractor is used for transportation of manure, although tractor is also used for other farm practices. Two variables namely farming experience and the distance from the city/market have significant negative relationship with the adoption of manure application.

Table 4

Private adaptation for private benefits and determinants.

Variables Manure applied Deep plowing

Age 0.04*     –0.03
(0.02)       (0.02)
Education –0.01       0.25***
(0.05)       (0.06)
Farming experience –0.08*** 0.04*
(0.02)       (0.02)
Family size 0.04       0.26***
(0.08)       (0.07)
Males above 15 years –0.03       –0.49***
(0.18)       (0.19)
Female above 15 years –0.22       –0.35
(0.23)       (0.22)
Owned land area –0.02       0.00
(0.02)       (0.01)
(0.07)       (0.05)
Tractor ownership 2.03*** 0.94**
(0.45)       (0.37)
Rotavator ownership 0.85
(1.17)
Distance from city –0.05**   –0.04
(0.02)       (0.02)
Constant –0.12       –2.10*
(1.15)       (1.19)
LR Chi2 62.67*** 47.81***
Observations 198       191

Standard errors in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1.

Table 5

Determinants of Private adaptation for public benefits.

Variables Bund-making Income diversification Crop diversification Land renting out

Age 0.02       0.06**   0.04       –0.02
(0.02)       (0.03)       (0.03)       (0.03)
Education 0.29*** 0.13       0.15*     –0.08
(0.06)       (0.08)       (0.08)       (0.08)
Farming experience 0.05**   –0.05*     0.02       0.00
(0.02)       (0.03)       (0.03)       (0.03)
Family size 0.39*** –0.04       0.16*     –0.07
(0.09)       (0.11)       (0.09)       (0.09)
Males above 15 years 0.33       0.27       0.73**   0.54*
(0.20)       (0.25)       (0.28)       (0.31)
Female above 15 years –0.13       0.28       0.56*     0.52
(0.24)       (0.32)       (0.31)       (0.35)
Owned land area 0.09**   –0.00       0.00       –0.34***
(0.04)       (0.06)       (0.02)       (0.07)
Livestock heads 0.04       1.82*** –0.04       0.13
(0.06)       (0.30)       (0.08)       (0.08)
Tractor ownership 0.68       –1.01       4.98*** –1.18**
(0.42)       (0.65)       (0.70)       (0.58)
Rotavator ownership –1.27       –0.90       –1.67       0.17
(1.07)       (1.18)       (1.56)       (1.46)
Distance from city –0.01       0.00       0.06*     0.01
(0.02)       (0.04)       (0.03)       (0.03)
Constant –8.68*** –4.79*** –8.74*** 4.59***
(1.59)       (1.68)       (2.14)       (1.71)
LR chi2       84.65*** 123.69*** 136.34*** 82.93***
Observations 198       198       198       198

Standard errors in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1.

Table 6

Estimates of adaptation practices on wheat yield.

Variables Coefficients Standard error

Constant 7.00       0.22
Age 0.01       0.00
Farming experience –0.01       0.00
Family size –0.05**   0.02
Male above 15 years 0.06*     0.03
Female above 15 years 0.06*     0.04
Renting out –0.24*** 0.09
Bund making 0.11       0.08
Manure application 0.04       0.08
Deep ploughing 0.07*     0.04
Crop diversification 0.25*** 0.08
Income diversification –0.08*     0.04
R2 0.15
F-test 3.79***
Observations 198

*** p < 0.01, ** p < 0.05, * p < 0.1.

Since crop production on the farms located in the rain-fed region depends on precipitation, conservation of moisture through deep plowing during the rainy season2 is the utmost important farming practice and it has become critical in the presence of climate change. Education of the respondents is significant at 1% level of significance and it has positive impact on adaptation of deep plowing, implying that increase in schooling years by 1%, increases probability of adapting deep plowing by 0.25%. Family size is positively related to adapting deep plowing and this variable is statistically different from zero at 1% level of significance. Number of males above 15 years age is negatively related with deep plowing. A positive and significant coefficient of tractor ownership (p < 0.01) implies that the respondents having tractors are highly likely in adapting deep plowing. The result was expected because tractor ownership makes it convenient for farmers to go for deep plowing.

### 4.3. Model results of private adaptation for public benefits

The results of the logit models for private adaptation for public benefits are given in Table 5. The coefficients of education, farming experience, family size and farm owned area are positive and statistically different from zero for bund-making adaptation. These results indicate a strong association between exposure to technology and adaptation. In case of income diversification, significant variables are age of the respondents, farming experience and livestock heads. Education, family size, number of adults, tractor and distance from the city are significantly associated with crop diversification. Land renting-out is another private adaptation for public benefits as this adaptation results not only in benefits to the owners of the farm but it also provides benefits to others having no and or a few acres of land. Owned land area, tractor and number of male above 15 years are significantly related with this adaptation measure to climate change. Variables namely tractor ownership and owned land area are negatively associated with land renting-out whereas number of males in the family has positive effect on land renting-out.

### 4.4. Effect of adaptation practices on wheat productivity

Results of multiple regression given in Table 6 show the effects of private adaptation on wheat productivity in the rain-fed district. Out of all adaptation practices in the present study, we find that renting out, deep ploughing, crop diversification and income diversification are statistically different from zero. Income diversification and land renting out are negatively related with wheat productivity. As expected, deep ploughing and crop diversification are found positively affecting wheat yield. In addition to adaptation practices, family size is significant and negatively associated with wheat yield. Males and females above 15 years significantly positively affect wheat yield.

## 5. Discussion

Human capital such as education, farming experience and family size is important determinant of deep plowing for moisture conservation, soil bund-making and income diversification. With high schooling years, the farmers have access to information relating to the best adaptation strategies. Significant coefficient of education variable implies its important role in adaptation to climate change. This is in line with Pali et al. (2002) and Mugi-Ngena et al. (2016) who found a positive influence of education on the soil water conservations and soil fertility management. Skoufias, Bandyopadhyay and Olivieri (2015) argue that education is strongly related with diversification in agriculture-related activities in India.

Farm mechanization is very low in Pakistan and it is particularly evident in the rain-fed areas of Punjab province where farmers have very small landholdings and their farm production depends on precipitation. Even tractors and basic implements are not sufficient to perform traditional farm practices. Statistics show that there are only 6315 tractors for 166 thousands rural households in Chakwal district (Government of Punjab, 2016), depicting that mostly farmers depend on their fellow farmers for tractor and other machinery services. Tractor ownership has strong role in adaptation to climate change as its ownership is positively related with deep plowing, manure application and crop diversification. All these strategies involve high use of farm machinery and farmers having tractor and other machinery. However, this variable is negatively related with land renting-out adaptation strategy. It is not economical for farmers having tractor and other farm machinery to rent out their farm area, as operating tractor and other machinery on the remaining farm area will not be economical. Owned land area variable has negative coefficient on renting-out adaptation. Negative relationship indicates that such farmers face greater difficulty in renting-out farm area as their staple food (wheat) heavily depends on farm production and they may not take risk of relying on staple food obtained from the fellow farmers, since the first priority for the small landholders is the staple food. Owned land area and soil bund-making are positively associated and this relation implying that farmers prefer to make investment in adaptation to climate change at their own land. Increasing owned land area by 1% will increase the probability of soil bund-making by 0.09%. This result is also in line with Anley et al. (2007) and Mugi-Ngena et al. (2016) that farmers with large farm size were found in investing water and soil conservation technologies.

Number of household members were significant in explaining the influence on deep plowing, soil bund-making and crop diversification. This shows that the farm households with large number of household members are more likely to have adaptation strategies in regard to the use of deep plowing, soil bund-making and crop diversification. These adaptation strategies are labor intensive practices and households with large family size can manage labor force requirement through their family members. This corroborates Dolisca et al. (2006), Anley et al. (2007), Nyangena (2007) and Mugi-Ngena et al. (2016) in that large family size enables farmers to take decision in favor of labor intensive adaptation strategies to climate variability.

Farmers make investment in adaptation practices with the goals of increasing productivity, income and overall welfare. The present study finds the relation between adaptation to climate change and wheat productivity. We find that adaptation practices namely land renting-out, crop diversification, income diversification and deep plowing are important contributors of wheat productivity. Negative sign of land renting out indicates that such farmers prefer to land renting-out instead of investing more in increasing wheat productivity. Crop diversification is another adaptation strategy showing a positive effect on wheat productivity. Farmers diversifying crops are inclined to allocate more resources in agriculture resulting in higher crop productivity whereas those diversifying income are more likely to divert their intension to other sources of earnings thus it may have negative impact on crop productivity. Since wheat productivity highly depends on rainfall and moisture available in the soil, deep plowing during the rainy season is critical in conserving soil moisture. Farmers following deep plowing receive higher wheat yield compared to their counterparts. Our results are in line with Akhter and Erenstein (2017) and Gorst et al. (2018) who find that adaptation to climate change is highly related with crop productivity in Pakistan.

## 6. Conclusions and policy implications

Adaptation to climate change is practiced by the farmers to mitigate the climate related risks. Adaptation through technology adoption can be based on decisions made by individual (private), community and public sector organizations. Private adaptation further can have benefits for individuals and public. The rain-fed agriculture in the north of Punjab is characterized with small landholdings, low farm mechanization, high dependence on precipitation, semi-hilly topography, and pre-dominant traditional farm practices.

Results of the study indicated that a host of socioeconomic factors of rural households in the rain-fed agriculture dictated farmers’ response to climate variability, studying the role of these factors is inevitable for designing solid policy interventions for adaptation to climate variability. Results of the study imply that the policy-makers, researchers and regional planners can build on this work by undertaking more interdisciplinary research approach to find the most suitable adaptation strategies at individual and community levels. This becomes vital for heterogeneous rural households because some households have better capacity in adapting to climate variability compared to their fellow farmers. This necessitates to tailor adaptation policies while considering different biophysical and socioeconomic circumstances.

Education and farming experience, being the significant factors influencing the use of adaptation strategies imply that awareness about adaptation strategies is important area to be focused in policy interventions for adaptation to climate variability. Education and training programs are already organized for the farmers by the Department of Agriculture, Government of Punjab.

Mostly adaptation strategies are labor intensive farm practices such as crop diversification and soil bund-making. Presently, farm machineries available in the market are particularly designed for large landholders. Small landholders are not able to afford and operate optimally considering few hectares of landholdings. Thus, there is a need to invest in farm machineries suited to small landholders. The policy makers should give due focus to public sector interventions in the form of research and development to help these resource scarce rain-fed farming communities and also find ways to support the private adaptations providing public goods for benefit of environment and the society at large. Future research should consider the nature of public sector adaptation efforts and the related distributional issues among community members. Moreover, the village commons are facing additional threats and demand for ever bigger contribution for sustainability. The future research should address climate change related additional costs and resultant benefits and their distribution to members to develop a multidimensional understanding about adaptation to climate change.

## Notes

1Irrigated area is primarily irrigated through ground or surface water in the province whereas unirrigated area mainly depends on rainfall.

2It is also important that deep plowing during dry season can cause evapotranspiration. We ensure that farmers follow deep plowing practice during the rainy season only to conserve moisture.

3Public benefits of private adaptation are not given in the present study. However, future study should consider these benefits.

## Competing Interests

The authors have no competing interests to declare.

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