Assignment

“Research seminar”

I am MUHAMMAD LAEEQ a student of M.Phil Economics is going to work

on research.

My area of research is:

“Impact of climate change on farming community of region Chakwal,

Punjab.”

My supervisor name :

Dr. ASIF ALI NAQVI

Government College University, Faisalabad.

Tentative Title:
Impact of Climate Change on Farming Community of District Chakwal,
Punjab.
Abstract

This study would investigates the perception of historic changes in climate and associated impact on local
agriculture. I draw empirical data obtained from farm household surveys conducted in Chakwal district.
Cereal crops (mainly rice and wheat) are crucial to ensuring the food security in the region, but
sustaining their productivity has become a major challenge due to climate variability and
uncertainty. This paper uses an integrated climate-crop-economic modelling framework (using
Ag MIP protocols) to make an integrated assessment of climate change and adaptation strategy
for rice-wheat growing farms. This framework enables integration of location- and farm-specific
climate and crop modelling output for assessing economic impact on the farming systems using
trade-off analysis (TOA-MD) model. The assessment clearly shows likely impact of climate
change on agricultural production systems, and how adaptation strategies can reduce climate
change vulnerabilities. The adaptation strategy for existing farming system shows positive
impacts on crop yield, farm returns and per capita income.

Introduction
The effects of gradual climate changes and extreme weather events in the recent past have
undermined progress in the alleviation of poverty and food insecurity, while also having a
negative effect on overall development efforts. Economic sectors that largely depend on weather
conditions – either directly or indirectly – most notably agriculture and fisheries are increasingly
subject to the impacts of climate change (IPCC, 2012).
The purpose of study is to assess the impact of extreme metrological events on the livelihood of
subsistent farming communities which are already on the margins of poverty or even verse, and
to suggest package to offset vulnerabilities resulting from these disasters. This study aimed to fill
a research gap by delineating the impact of CC extreme event on different socio-economic
indicators of farming communities for different agro-ecological zones of Punjab which are more
prone to this problem.
Scope of study

The purpose of study is to assess the impact of extreme metrological events on the livelihood of
subsistent farming communities which are already on the margins of poverty or even verse, and
to suggest package to offset vulnerabilities resulting from these disasters. This study aimed to fill
a research gap by delineating the impact of CC extreme event on different socio-economic
indicators of farming communities for different agro-ecological zones of Punjab which are more
prone to this problem.

Proposed objectives:
1. To assess the impact of climate change on the poverty, per capita income and mean
net farm return of farmers of the study area
2. To assess the last two decade variations in the climate and their impacts on the
livelihood of respondents
3. To suggest policy recommendations in the light of investigated results

Review of literature

Adger, W. N. (2000). stated that In Kenya (as with regions worldwide) climate change and
variability drives changes in weather patterns and causes seasonal shifts (Republic of Kenya
2010). Changing weather patterns and seasonal shifts act as stresses on the agricultural
ecosystems, compromising the production of agricultural goods and services. In Kenya, close to
70% of the population depends directly on agriculture for their livelihoods, and therefore any
interference or disturbance to agricultural ecosystems is likely to have adverse impacts on the
rural areas. Such impacts could include reduced farm returns, reduced house hold incomes, and
increases in poverty levels.

Howden and Hayman (2010) examined the impact of projected climate change on the Goyder
line in South Australia. This line historically has represented the border of cropping viability.
They show (see Figure 5) the large uncertainty about the future position of this line, with there
being a small probability of the line moving inland yet a higher probability of it moving south or
coastwards, thereby reducing the area viable for cropping in South Australia.

The impact of climate change on cropping area was also investigated in an earlier study by
Reyenga et al. (2008). They noted that climate change would likely alter the distribution of
cropping in Australia, given the importance of climate and soil characteristics in determining
average yields and the frequency of failed sowings. They suggested that the viability of some
cropping regions across Australia would decrease if the number or sequence of poor seasons
increased.
Thornton et al. (2008) mapped climate vulnerability with a focus on the livestock sector. The
areas they identified as being particularly prone to climate change impacts included arid-semiarid
rangeland and the drier mixed agro-ecological zones across the continent, particularly in
Southern Africa and the Sahel, and coastal systems in East Africa. An important point they raise
is that macro-level analyses can hide local variability around often complex Responses to climate
change.
Antle et al. (2004) examined relative and absolute economic measures of the vulnerability of
dryland grain farms in Montana to climate change with and without adaptation using data from a
statistically representative sample of farm fields. That study allowed inferences to be drawn
about the vulnerability of a heterogeneous population of farms to climate change with and
without adaptation, and showed that when both climate change and higher atmospheric CO2
concentrations are taken into account, average crop enterprise return was higher relative to the
baseline climate for five and lower for three of the eight adaptation scenarios evaluated.
Although Antle et al. (1999, 2004) evaluated the potential impacts of climate change on crop
yields and crop enterprise returns, they did not consider potential impacts of future climate
change on NFI as does this study.
Reilly (2002) used the Hadley Center and Canadian climate models to estimate potential impacts
of climate change on 2030-2090 crop yields for the entire US. He found that future climate
change could result in higher yields for cotton, corn for grain and silage, soybeans, sorghum,
barley, sugar beets, and citrus fruits. Higher or lower yields for wheat, rice, oats, hay, sugarcane,
potatoes, and tomatoes, depending on the climate scenario. Large increases in average grain
yields for the northern half of the Midwest, West, and Pacific Northwest. Depending on the
climate scenario and time period, either increases or decreases in crop yields in other regions of
the US. Large reductions in crop yields in the South and Plains States for climate scenarios with
low precipitation and substantial warming. Kaiser et al. (1993) evaluated the economic and
agronomic impacts of several climate warming scenarios, mainly temperature changes, on a
grain farm in southern Minnesota and alternative ways to adapt the farm to those scenarios. That
study did not evaluate the impacts of other climate variables, such as precipitation and
atmospheric CO2 concentration, on crop yields as does this study.
Antle et al. (1999) evaluated the impacts of climate change on crop enterprise returns in the
Great Plains. That study showed that with adaptation of crop enterprises to climate change,
climate change and CO2 enrichment caused mean crop enterprise return to change by -11% to
+6% and variability in crop enterprise return to increase 7–25% relative to the baseline climate
and without adaptation, mean crop enterprise return decreases 8–31% and variability in crop
enterprise return increases 25–83% relative to the baseline climate.

Methodology

The Tradeoff Analysis Model for Multi-Dimensional Impact Assessment (TOA-MD) is a

parsimonious, generic model for analysis of technology adoption (e.g. adaptation strategies),
impact assessment (e.g. climate change), and ecosystem services analysis.The TOA-MD
represents the whole farm production system (i.e. includes crops, livestock and aquaculture sub-
system, and the farm household characteristics). The TOA-MD is a model of a farm population,
not a model of an individual or “representative” farm. Accordingly, the fundamental parameters
of the model are population statistics – means, variances and correlations of the economic
variables in the models and the associated outcome variables of interest. With suitable bio-
physical and economic data, these statistical parameters can be estimated for current systems.
Using established methods we can estimate how the TOA-MD model parameters would change
in response to climate change or technological adaptations. These changes in model parameters
are the basis for the climate impact, vulnerability and adaptation analysis.

Proposed Statistical and econometric Techniques ( software) :

1. TOA -MD (Tradeoff Analysis Model for Multi-Dimensional Impact Assessment)

Dependent Variables: poverty, per capita income, mean net farm returns, livelihood.

Independent Variable: farm size, house hold size, house hold members, yield from
system, standard deviation of net farm returns, price of input, price of output, variable
production cost.

Data type: field survey, secondary data, simulation data

Data Source: primary data collected by field questionnaire

Outcomes:

 Climate change might be effect the yield of crops.

 It can also have an positive effects on mean net farm returns and per capita
income.