Cotton yield and Climate Change Adaptation in Pakistan: Application of Multinomial Endogenous Switching Regression Model

Abstract

Purpose: Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study.
 Design/Methodology/Approach: This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework.
 Findings: Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield.
 Implications/Originality/Value: For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

Assessing the Impact of Better Cotton Production Initiatives Taken by Small Farmers in District Tando Allahyar, Sindh, Pakistan within the Context of the Circular Economy

“…Previous research may provide valuable information and insight to research technique. Siddiqua et al, (2021), examined the impact of different farm management practices on reducing the impact of climate on cotton yield by using multinomial endogenous switching in Sindh and Punjab. They documented negative impact of climate change on cotton yield and affirmed the importance of various climate change techniques in increasing cotton yield.…”

Recently Emerging Trends in Big Data Analytic Methods for Modeling and Combating Climate Change Effects

“…The statistical model is known for its structural simplicity. The kind of statistical models deployed for the assessment and evaluation of climate change data include Regression, Bivariate, Frequency Ratio (FR), Evidential Belief Function (EBF), and Ordered Weight Average (OWA) that has been widely utilized [29], [30]. For instance, [29] combined different statistical models (e.g.…”

“…These, however, showed over 40 years, from 1981-2020 the positive effect of climate change on corn yield, with temperature having a major effect compared to precipitation. More so, statistical models show off a great evaluation to assess and make future projections on the best approach, simulations, strategy, measures, and policies in different hemispheres and states such as Indiana [33], Jemma sub-basin, upper Blue Nile Basin of Ethiopia [34], Scotland's Atlantic salmon rivers [35], and regions of Pakistan [30]. However, the current data generation is generic and spontaneous and requires advanced data analysis libraries or techniques to handle.…”

“…(ii) change selection methods Selecting climate change analytics methods [4], [18], [30], [36], [44], [55], [64] is di cult due to the nature of the model to be used. Each method requires extensive and indebt training.…”

Threats and Challenges for Sustainable Cotton Production in Sindh, Pakistan: Climate Change Vulnerability Assessment and Adaptation to Combat Climate Change

“…Climate change and climate extremes conditions have a negative impact on livelihood of the cotton growers as heat waves, drought and unavailability of quality inputs affects the cotton production and yield which ultimately affects their livelihood. Many studies in recent past related to cotton and socioeconomic conditions have been conducted related to cotton and livelihood of the cotton farmers (Siddiqu et al, 2021;Ahmad et al, 2021; but the individual and scattered factors were studied. Sustainable and climate smart cotton production is required to meet the country demands, where climate resilient production technology is required to save the cotton from climate extremes.…”