research paper on cotton production in pakistan

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• Published: 08 January 2021

Cotton germplasm improvement and progress in Pakistan

• RAZZAQ Abdul   ORCID: orcid.org/0000-0002-0106-0481 1 , 2 ,
• ZAFAR Muhammad Mubashar 1 ,
• ALI Arfan 3 ,
• HAFEEZ Abdul 1 ,
• BATOOL Wajeeha 3 ,
• SHI Yuzhen 1 ,
• GONG Wankui 1 &
• YUAN Youlu 1  

Journal of Cotton Research volume  4 , Article number:  1 ( 2021 ) Cite this article

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Cotton ( Gossypium spp.) contributes significantly to the economy of cotton-producing countries. Pakistan is the fourth-largest producer of cotton after China, the USA and India. The average yield of cotton is about 570.99 kg.hm − 2 in Pakistan. Climate change and different biotic stresses are causing reduction in cotton production. Transgenic approaches have unique advantage to tackle all these problems. However, how to confer permanent resistance in cotton against insects through genetic modification, is still a big challenge to address. Development of transgenic cotton has been proven to be effective. But its effectiveness depends upon several factors, including heterogeneity, seed purity, diffusion of varieties, backcrossing and ethical concerns. Cotton biotechnology was initiated in Pakistan in 1992–1993 with a focus on acquiring cotton leaf curl virus (CLCuV)-resistant insect-resistant, and improving fiber quality. This review summarizes the use of molecular markers, QTLs, GWAS, and gene cloning for cotton germplasm improvement, particularly in Pakistan.

Cotton is a cash crop in cotton-producing countries and is the second most important crop since it contributes significantly to the economy of Pakistan. Since its cultivation, cotton industry has endured serious external pressure of climate change, biotic and abiotic stresses, leading to a substantial economic loss in the country. Therefore, a continuous effort in cotton sustainable production is crutial to meet the requirements of domestic and export demand. Commercially, cotton is getting popular among farmers compared with several other crops. Hence, different techniques, strategies and policies are considered to develop new and resistant germplasms for sustainable development of cotton industry in Pakistan.

Introduction

Cotton is known as a “white gold” in cotton-producing countries, and it was grown over 33 million hectares in 2019 worldwide (Tarazi et al. 2020 ). Cotton consumption is increasing, corresponding to a tremendous increase in population around the globe (FAO 2019 ). Pakistan is an agricultural country, and cotton is the second most important crop with a significant role in its economy. It contributes 0.8% in the overall GDP and an additional 4.5% in agriculture value addition (Rehman et al. 2019 ). During 2018–2019, a decrease of 17.5% was seen with an overall production of 9 861 million bales versus11 946 million bales in 2017–2018. This decrease in cotton production was due to the decrease in incentives to the farmers compared to the previous year, leading to the shrinkage in cultivation area from 2 700 thousand to 2 373 thousand hectares (Economy Survey of Pakistan 2018–2019). Pakistan is ranked fourth in cotton production around the world. Since the introduction of cotton biotechnology in Pakistan in 1992–1993, several measures have been taken into account to improve its quality and yield. Breeding programs, cloning, cotton transformations, utilization in germplasm sources and molecular markers-based technologies have been discussed coupled with the increase in capacity building of various funding and research agencies.

Cotton germplasm

Cotton ( Gossypium spp. ) belongs to Malvaceae family and is found on the Indian subcontinent, America and Africa. The end product of cotton is fiber, a backbone of the textile industry, which develops through a series of processes in bolls after pollination of flower (Bakhsh et al. 2009 ). The major genus of cotton is Gossypium containing 50 different species, out of which only four are commercially grown for agricultural use (Wendel and Cronn 2003 ). The major grown species is G. hirsutum L. contributing almost 80% of the total cotton production in Asia. The genetic resources of cotton are immense and dispersed over five continents with classification as primary, secondary and tertiary germplasm pools (Wendel et al. 1994 ). The ploidy characters of the Gossypium genus show a big variation which makes its classification quite difficult. Many researchers have given the classification of this genus, but the most widely accepted one is done by Chen and Gallie ( 2004 ) which is based upon chromosomal paring affinities. A total of 50 species of cotton have been classified of which 45 species have been reported as diploid (2n = 26) and 5 species are reported as tetraploid (2n = 52) (Chen et al. 2014 ). Mexico is considered to be the center of origin of G. hirsutum and it has spread over Central America and the Caribbean. According to the archaeo-botanical survey, G. hirsutum is domesticated within the Mesoamerican gene pool. (Wendel et al. 1994 ; Brubaker et al. 1999 ). Although, Asiatic or desi cotton ( G. arboreum ) gives low yield, it has many important agronomical characteristics, e.g. good fiber strength with remarkable plasticity, showing better insect resistance and stronger capacity to grow under poor growing conditions than G. hirsutum .

Cotton germplasm resources in Pakistan

There are many countries with a history of cotton germplasm production such as China, Brazil, the United States, India and Pakistan (Robinson et al. 2007 ). G. arboreum is indigenous to Pakistan and has been evolved from G. herbaceum L. (Rehmat et al. 2014; Hutchinson 1954 ). These genotypes have been characterized by morphological, agronomical and physiological features showing tolerance against drought and insect/pest (Rehmat et al. 2014).

Evolution of G. arboreum L. in Pakistan

Due to limited cross-pollination or mixing of the seeds, most of the cotton germplasm of G. arboreum has been obtained within several variants. The natural selection from a single population resulted a narrow genetic base of the cultivars. Initially, two cotton varieties such as Z. Mollisoni and 278-Mollisoni were developed to replace old varieties. The Cotton Research Institute (CRI) was established in Faisalabad for the improvement of varieties, and it was initiated by Trought T. and later on by Afzal M. The improved 15-Mollisoni cotton line was tested in national trials for 13 years and approved for cultivation in 1930 concerning its high ginning outturn (GOT) of 35% compared with 34% for Mollisoni and 33% for the mixture cultivated in the farmer’s field. Another variety 39-Mollisoni was tested, which showed 36% ∼ 37% GOT as compared with 35% of 15-Mollisoni (Rahman et al. 2012 ).

Cotton varieties developed by local crossovers

The Bt cotton variety CIM-775 was selected in the crosses of local cultivars and accessions from United States Department of Agriculture (USDA) National Plant Germplasm System, and this variety secured 2nd position based on yield performance among 102 varieties in the National Coordinated Varietal Trials (NCVT) conducted in 2019–2020. This is a cotton leaf curl virus (CLCuV) tolerant variety and has an increased yielding potential of 50–60 pounds per hectare with a staple length of 28.6 mm, a lint percentage of 39.5% and a micronaire value of 4.3. The Bt variety CIM-303 was also developed by crossing United States Department of Agriculture (USDA) National Plant Germplasm System Accessions to local germplasm materials, and it shows promising results on CLCuV tolerance. The cotton varieties developed by crossing NIAB 999 and NIAB 111 were early maturing, high yield, and heat and CLCuV tolerance.

The mutant cotton variety Chandi 95 was approved in 1982. It was developed by irradiation of gamma radiations (300 Gy). The cotton variety NIAB 78 was developed by irradiation-induced mutation and was a derivative of (AC-134 × Deltapine) F 1 . It was approved by Punjab Seed Corporation (PSC) for general cultivation in Punjab in 1983. The introduction of this variety increased the yield from 3 million bales in 1983 to 12.8 million bales in 1991–1992. In 2008, cotton variety NIAB-846 approved by PSC was developed by the crossing of NIAB 78 and REBA 288 (pollen irradiated with 10 Gy of gamma rays). This variety was resistant to CLCuV and CLCuV-B (Burewal strain) and had heat tolerance. NIAB 777 was approved in 2009, and has resistance against CLCuV-B and is suitable for high planting density as shown in Table  1 .

CRI Faisalabad had been carrying out a breeding program for desi cotton at different research stations, including Haroonabad where the breeding practices started in 1952 in the drought prone area. Based on leaf morphology and color, four candidate lines were identified. Among different tested varieties, 73/3, was found superior with 42% of GOT and a staple length of 13.7 mm compared with an cultivated mixture with 37% ∼ 38% GOT and 16 ∼ 19 mm staple length. It was noted that the newly developed varieties showed yield compared with that of the cultivated mixture of desi cotton. These observations led the breeders to abandon the varietal improvement by selection (Campbell et al. 2010 ). G. arboreum was replaced by high yielding G. hirsutum, and then several cotton varieties were developed in Pakistan by hybridization and mutations. Some approved varieties of Bt and non-Bt cotton in Pakistan are listed in Table  2 .

Genetic engineering and biotechnology have played a vital role in the development of transgenes application and the overall economy boosting of Pakistan through agriculture. Plant biotechnology has enabled the researchers to incorporate foreign genes that control different traits such as drought resistance, fiber quality, herbicide resistance, CLCuV resistance and pest resistance. The rapid increase in genetically modified (GM) cotton enhances the planting acreage and productivity of many countries around the world, including Pakistan.

Central Cotton Research Institute (CCRI), Multan is striving hard to develop new cotton varieties with tolerance to stresses and desired fiber traits. The CCRI has maintained 6 030 accessions of four cotton species. Many varieties have been approved for cultivation during the last few decades. For example, Bt.CIM-598 has been approved for cultivation in Sindh province. Bt-CIM-632 and Bt-CIM-610 have completed their 2 years trial in NCVT while Bt-CIM-663 and Bt-CIM-343 have completed their first year trial in NCVT. The CCRI evaluated 33 Bt lines and 15 non-Bt lines at Multan and Khanewal on desirable characteristics, and the data were presented during the 77th expert sub-committee in March 2018. The exotic lines Mac-07 and AS-0349 were crossed for resistance against CLCuV in filial generations.

Pakistan Central Cotton Committee (PCCC) was established to create funds for the development and marketing of cotton. The PCCC developed many different cotton varieties in 1985–2018 as listed in Table  3 .

Molecular marker technology in cotton

Molecular marker is very useful for molecular characterization and identification of genetic variation, and has been used in the marker-assisted selection (MAS) and genome fingerprinting (Kalia et al. 2011 ). The molecular markers are important in genomics research because they may or may not link with the phenotypic expression of a character in an organism (Agarwal et al. 2008 ). In cotton genomes, the most important molecular markers are polymerase chain reaction (PCR)-based markers because of their high effectiveness and utilization which include inter simple sequence repeats (ISSRs) (Reddy et al. 2002 ), amplified fragment length polymorphism (AFLP) (Abdalla et al. 2001 ; Alvarez and Wendel 2006 ), simple sequence repeats (SSRs) (Liu et al. 2000 ; Zhu et al. 2003 ) and random amplified polymorphic DNA (RAPD) (Tatineni et al. 1996 ; Xu et al. 2001 ; Lu and Myers 2002 ). Among the genomic resources, there are about 16 162 SSRs and 312 mapped cotton RFLP sequences available publicly. The RFLP, SSR, AFLP, AFLP and RAPD markers have been applied in different mapping populations to develop linkage maps. It has been reported that the identification of DNA markers is associated with over 29 important traits such as fiber quality and yield, leaf and flower morphology, trichomes density and their distribution, and disease resistance (Rahman et al. 2012 ).

The advent of cotton leaf curl virus (CLCuV) was proved to be a compelling factor to design novel strategies for cotton breeding programs in Pakistan. Before the occurrence of CLCuV epidemics, the genetic similarity among the elite cotton varieties in Pakistan ( Gossypium spp. ) was 81.5% ∼ 93.41%. New cultivars were developed by crossing the exotic resistant germplasm with the germplasm that susceptible to CLCuV (Rehman et al. 2012). The study was designed to assess the genetic diversity or genetic relatedness among the newly released, extremely resistant and medium-resistant cultivars. Different methods such as field evaluation, whitefly-transmission studies, grafting, dot-blot hybridization, and multiplex-PCR using conserved primer sequences were employed for the screening of 27 cotton genotypes. Twenty extremely resistant and resistant cultivars were selected for DNA-RAPD analysis. The genetic similarity of exotic germplasm with the elite cultivars was found in the range of 81.45% ∼ 90.59%. Similarly, the genetic relationship among the elite cultivars was 81.58% ∼ 94.90%. However, the average genetic similarity among all the studied genotypes was 89.55%. It was concluded that only cultivar VH-137 possessed a diverse genetic background. The study also emphasized breeding for high genetic diversity to serve as a buffer against potential epidemics (Rahman et al. 2002 ).

The CLCuV has resulted in a significant loss in the economy of Pakistan and its rapid transmission is a major threat to the neighboring cotton-growing countries such as China and India. In a current study, a total of 10 cotton genotypes of different tolerance levels were taken from the cotton germplasm resource available at the National Institute of Biotechnology and Genetic Engineering (NIBGE), Faisalabad-Pakistan. In total, 322 SSRs derived from bacterial artificial chromosome end sequences of G. raimondii were screened out. Of the total, 65 primer pairs were identified as polymorphic, and the genetic similarity was found in the range of 81.7% ∼ 98.7%. Among the polymorphic markers, only two SSR markers, PR-91 and CM-43 were amplified in the CLCuV-tolerant genotypes which showed significant association with tolerance to the disease (Abbas et al. 2015 ).

The epidemic virus has spread to the cotton-growing countries including the USA, China, Pakistan, and India. Both pathogenic and non-pathogenic approaches for the development of transgenic cotton are in progress. The use of DNA markers overlaid with transgenics and CRISPR/Cas system for the insertion of resistant markers into adapted cultivars would be instrumental to counteract the disease caused by CLCuV (Rahman et al. 2017 ).

A summary of the genomic diversity, evaluated with genetic markers in Pakistan and major cotton-growing countries is described in Table  4 .

Status of marker-based improvement of cotton in Pakistan

Recent advances in cotton biotechnology such as DNA sequencing technology and genome editing have changed the path of marker-based crop improvement. This has given valuable standards with improved status of plants (Yin et al. 2019 ). These techniques facilitate the discovery of SNPs which are useful and extremely saturated markers for cotton genomic research. In summary, all the markers have their own advantages and limitations and the choice of these markers depends on the selected trait and the nature of the work. Based on the SSR marker polymorphism analysis, cultivars released in Pakistan since 1914 showed relatively low genetic diversity (Khan et al. 2010 ).

Cotton QTL studies in Pakistan

There are many factors that determine the fiber quality, including fiber color, strength, length, micronaire, and corresponding yield (Lokhande and Reddy 2014 ). A certain advancements have been made in the spinning performance of cotton fiber that increases the fiber quality and yield-associated parameters to a significant level (Kohel et al. 2001 ). In cotton breeding, the most crucial issue is the negative correlation between lint yield and fiber quality (Zhang et al. 2003 ; Shen et al. 2007 ). Therefore, to identify the regions of linked genes related to lint yield and fiber quality in the cotton genome and to develop the linkage maps, researchers utilized the technique of QTL analysis. The first QTL mapping was reported in 1996 and a huge array of QTLs has been identified by utilizing molecular markers technology. Till now, several databases have been developed. “Cotton Gen” is one of them that contains the information of 988 quantitative loci for 25 diverse characters (http://www.cottongen.org/data/qtl). Currently, 20 QTLs are recognized for fiber attributes (Shang et al. 2015 ) and these attributes were associated with 59 loci (Tan et al. 2015 ). This progress allows the cotton breeder to improve yield and yield-related parameters and contribute to the economy of the region. The QTLs identified in cotton germplasm using different marker technologies are summarized, and are commonly used for the genetic improvement of various agronomic traits in Pakistan. A total of 185 cotton genotypes of lint traits were selected and studied at different locations for 3 consecutive years. The genetic variations were evaluated for different traits and IR-NIBGE showed a maximum of 43.63% ginning out turn (GOT). The Ward’s method was used for the clustering of genotypes. Out of 382 SSRs, 95 polymorphic SSR primer pairs were surveyed on 185 genotypes. A total of 75 markers associations were calculated, and out of which only MGHES-51 was found associated with all traits. This study indicates that the high frequency of favorable alleles in cultivated varieties is possibly due to the fixation of the desired alleles by domestication. These alleles can be further exploited in marker-assisted breeding or gene cloning through the next-generation sequencing tools (Iqbal and Rahman 2017 ).

Saleem et al. ( 2018 ), from the Department of Plant Breeding and Genetics, BZU-Multan reported drought-tolerant QTLs identified using DNA markers (NAU-2954, NAU-2715, NAU-6672, NAU-8406, and NAU-6790) among 44 drought related varieties in Pakistan. The varieties were cultivated to observe the features of the excised leaf water loss, relative water contents and cell membrane stability under drought stress. It was noted that chromosome 23 harboring QTL- qtl RWC-1 for relative water content linked with marker NAU-2954, confers drought tolerance in cotton. Furthermore, the variety of CRIS-134 which was subjected to the marker-assisted selection (MAS) contained all previously identified QTLs related to drought tolerance (Saleem et al. 2018 ).

Additionally, the National Institute of Biotechnology and Genetic Engineering (NIBGE) screened 322 SSR markers derived from bacterial artificial chromosome end sequences of G. raimondii . When PR-91 and CM-43 were amplified, they showed an association of resistance against CLCuV (Abbas et al. 2015 ). Using RAPD markers, two QTLs for relative water content were detected with the nearest markers NAU2954 and NAU2715, respectively, each on chromosome 23 and 12 at Department of Plant Breeding and Genetics BZU-Multan (Saleem et al. 2015 ). The RAPD-DNA was employed with three primers: OPO-19, OPQ-14 and OPY-2 for the assessment of genetic diversity of 18 cotton varieties in Pakistan. The primers revealed amplification with the product sizes of 470 bp (base pair), 325 bp and 10 701 bp with a selection efficiency of 27.7%, 67.1% and 44.4%, respectively (Mumtaz et al. 2010 ). Similarly, Diouf et al. identified 1709 genes with 4 QTLs that are present in two regions named as cluster 1 and cluster 2 (2018). Among 1709 genes, only 153 showed higher expression levels than those of the remaining genes with lower expression in all fiber development stages. Furthermore, five important genes playing a vital role in the development of fiber were also identified, namely Gh_D03G0889, Gh_D12G0093, Gh_D12G0969, Gh_D12G0410, and Gh_D12G0435 (Diouf et al. 2018 ).

Hence, it concluded that the QTL technique led to two complementary uses (Prioul et al. 1997 ): the first one focused on those QTLs that target the physiological components of macroscopic characters whereas the second is marker-assisted breeding (MAB). They are used for the tagging and analysis of pyramid favorable alleles and also break their linkage with unfavorable genes in cotton (Lee 1995 ; Ordon et al. 1998 ; Ribaut and Hoisington 1998 ).

Genome-wide association studies (GWAS) in Pakistan

Linkage dis-equilibrium (LD) mapping, also known as association-mapping, is an effective way to discover the dissimilarity in complex characteristics by using historical and evolutionary recombination operations at the population level (Nordborg and Tavaré 2002 ). GWAS is an important tool that is used to recognize QTLs and dissect the genetic control of complex quantitative characters (Saeed et al. 2014 ; Islam et al. 2016 ). To recognize the characteristics that linked with genetic markers, non-structural populations are phenotyped and genotyped in association-mapping (Myles et al. 2009 ). The association-mapping for cotton aids a large-scale utilization of natural genetic diversity conserved within the cotton germplasm (Abdurakhmonov 2007 ). Abdurakhmonov et al. analyzed genome-wide LD and association-mapping of fiber-related characters in 285 exotic accessions of cotton using 95 SSRs markers ( 2008 ). Similarly, 202 SSRs were used for the LD-based association-mapping for fiber quality characters in 335 cotton genotypes (Abdurakhmonov et al. 2009 ).

Gene cloning

Map-based gene cloning is a fundamental approach for exploitation and recognition of quantitative agronomic characters. The conventional map-based cloning techniques are efficient, but are laborious and time-consuming due to the complex genome of cotton (Zhu et al. 2017 ). Therefore, different techniques are developed to explore the function of genes of interest and how these genes are successfully transformed into crops. In cotton, the development of new transformation vectors and new strategies related to gene cloning and gene editing provides a great opportunity to transform new characteristics and improve yield-related traits that are not possible to develop through conventional methods. These characters include herbicide (Bayley et al. 1992 ) glyphosate resistance (Zhao et al. 2006 ), reduction of gossypol content in cottonseed (Sunilkumar et al. 2006 ) and resistance to bollworm (Rashid et al. 2008 ) and aphids (Wu et al. 2006 ). A milestone in cotton research was the development of the genetically modified organism (GMO) cotton that contains Bacillus thuringiensis (Bt) gene. Globally, the transgenic cotton is grown in an area of more than 33 million hectares (Tarazi et al. 2020 ). To produce an ideal transgenic plant, an appropriate gene construct is necessary. For this purpose, besides the desired gene, the vector also include a reporter gene, selection markers, an appropriate promoter for gene expression and terminator to make an efficient transformation. CaMV35S ( Cauliflower mosaic virus) promoter is a constitutive promoter that is widely used in transgenic cotton. The selectable marker genes (antibiotic and herbicidal-resistant genes, anti-metabolic genes) and reporter genes (green fluorescent protein (GFP), beta-galactosidase (LacZ), luciferase (Luc), chloramphenicol acetyltransferase (CAT), and beta-glucuronidase (GUS)) are helpful to detect the plant which has transgene expression (Zapata et al. 1999 ). Furthermore, a series of pCAMBIA vectors are largely used worldwide, and they are commonly used in Pakistan for gene cloning in cotton. The presence of kanamycin and bialaphos herbicide resistance genes as selection markers and GUS or GFP as reporter genes in these vectors makes them more efficient and unique in nature. Zapata et al. ( 1999 ) reported the use of gramineous expression vectors pGU4AGBar and pGBIU4AGBar (Hou et al. 2003 ; Rao et al. 2016 ). Some successful transformations along with promoter and other essential elements are described in the gene transformation section.

Gene transformation approaches in cotton

Many methods are used for genetic transformation in cotton that have their own advantages and limitations but Agrobacterium and microprojectile bombardment are currently the most commonly and widely used procedures for gene transformation (Dai et al. 2001 ). In the past decade, scientists developed genetic transformation techniques in cotton ( G. hirsutum ) using Agrobacterium -mediated shoot apex cut method and sonication-assisted Agrobacterium -mediated transformation. Agrobacterium -mediated gene transformation method became the most reliable and best method to generate transgenic cotton in Pakistan. It ultimately changed the way of DNA delivery but also confirmed the expansion of efficient transforming vectors.

Efficient methods and successful genetic transformations in cotton

Different protocols have been used, such as meristem transformation (Gould et al. 1991 ; McCabe and Martinell 1993 ; Zapata et al. 1999 ) via either the gene gun or Agrobacterium for transformation in cotton plants.

Agrobacterium -meditated gene transformation

Agrobacterium -mediated gene transformation has been the most preferred transformation method used for the transformation of foreign genes such as Cry1Ab and Cry1Ac genes of Bacillus thuringiensis into cotton to develop insect-resistant transgenic plants (Singh et al. 2004 ). For example, Mao et al. developed an insect-resistant transgenic cotton expressing dsCYP6AE14 using explant genetic transformation of the hypocotyl and cotyledon ( 2011 ). A certain cotton cultivars have been transformed using this technique and plants have been regenerated later by using embryogenesis; however, commercially important varieties have been proved recalcitrant because of their inability to develop embryogenic tissues. The chloroplast localization of Cry1Ac and Cry2A protein was successfully achieved in cotton. And 100% mortality was obtained in the 2nd instar larvae of the targeted insect after feeding for 72 h (Muzaffar et al. 2015 ).

A local cultivar of cotton, MNH-786, was manipulated with pKian-1 and the stable incorporation of the TP-Cry1Ac-RB construct in putative transgenic plants was confirmed by polymerase chain reaction (PCR) while fusion-protein expression in the chloroplast as well as in cytoplasm was proved using the western blot analysis. It has been confirmed that hybrid-Bt protein is expressed within the chloroplasts using confocal microscopy of leaf-sections (Kiani et al. 2013 ).

Furthermore, the cultivar MNH-786 was modified by the transformation of herbicide and insect resistance genes. The Cry1Ac + Cry2A and GT (herbicide resistant) genes were cloned in a different cassette using 35S promoter. The apex portions of mature embryos of MNH-786 cultivar were injured with a blade and infected with the strain of Agrobacterium tumefaciens containing transgene constructs. Cotton plants transformed, were acclimatized in pots and later were grown under greenhouse conditions. The - PCR and ELISA assured the presence of the transgene and expression of its protein in the transformed plants. Transformation efficiency was 1.05%. All larvae of Helicoverpa armigera feeding on leaves of transgenic cotton of T 0 generation were found dead as compared with the larvae feeding on leaves from non-transgenic cotton (Awan et al. 2015 ).

Two Bt genes including cry1Ac and cry2A were pyramided in a local cotton variety CIM 482 by sonication-assisted Agrobacterium -mediated transformation (SAAT). The insect bioassay showed promising results and 75% to 100% mortality of H. armigera was observed in transgenic plants. The results obtained explained that one vector carrying two Bt insecticidal genes with the same promoter is proving to be valuable for future breeding programs (Rashid et al. 2008 ). Ali et al. tested two cotton varieties CRSP1 and CRSP2 for genetic transformation efficiency concerning GT gene and insect mortality ( 2016 ). Their results exhibited that CRSP-1 has a valuable resistance against insects and weeds. They further reported that this may be helpful for farmers as well as national breeders to develop potential cultivars. The CpEXPA3 gene taken from Calotropis procera was introduced into a local cotton cultivar (NIAB-846) using strain LBA 4404. The results showed that fiber strength was greater in transformed cotton plants compared with that in non-transformed plants (Bajwa et al. 2013 ).

Particle bombardment-mediated transformation

Biolistic transformation has been found useful for the successful introduction of gusA , nptII , amino glycoside phosphotransferase ( apha-6 ), acetoacetyl- CoA reductase ( phaB ), and polyhydroxyalkanoate synthase ( phaC ) genes into cotton (McCabe and Martinell 1993 ; John and Keller 1996 ; Kumar et al. 2004 ). However, there are some disadvantages including low transformation frequency of explants, high frequency of epidermal transformations chimeras, and insertion of fragmented transgenes (De Block 1993 ; Depicker and Van Montagu 1997 ; Wilkins et al. 2000 ).

Other successful transformation methods in cotton

Different methods were also used for genetic transformation in the cotton crop. An introduction of exogenous DNA in self-pollinated flowers of cotton plants was reported by Zhou et al. ( 1983 ) using the pollen tube pathway method of transformation. Huang et al. ( 1999 ) and Lu et al. ( 2002 ) reported that transgenic cotton plants showing the green fluorescent protein gene and cellulose synthesizing genes ( acsA , acsB , acsC , and acsD ) of Acetobacterxylinum were produced by using these types of approaches.

Some successful transformations for agronomic traits in Pakistan

It has been reported that production of non-Bt cotton is continuously decreased by 35% ∼ 40% every year due to the attack of the insect pests which is an alarming situation for the cotton growers in Pakistan (Masood et al. 2011 ). Bt cotton was first registered by the Government of Pakistan in 2009 and was first grown in 2010 (Abdullah 2010 ).

The phytochrome B gene was transferred in the cotton crop by using the Agrobacterium technique. It was observed that the photosyntehsis rate of transgenic plants showed two times higher than the normal plants, and the transpiration rate and stimatal conductance was four-times higher. Data were recorded in the greenhouse and the field for two generations. It was also observed that there is a 35% increase of yield in transgenic cotton due to over-expression of the phytochrome B gene. This gene showed pleiotropic effects as a decrease in apical dominance and an increase in boll size.

Glyphosate-tolerant plants were also generated by transferring the 5-enolpyruvilshikimate-3-phosphate synthase ( CP4-EPSPS ) gene by using the Agrobacterium technique (Nida et al. 1996 ). These plants were found successful in the field, but 12 weed species resistant to glyphosate emerged after the application of herbicide for weed control (Dill et al. 2008 ). Genes encoding Cry proteins of B. thuringiensis have been classified as CryI , CryII , CryIII , CryIV , CryV , and CryVI based on their insecticidal activities (Crickmore et al. 1998 ; Wilkins et al. 2000 ; Siebert et al. 2008 ). Cottonseed is an important source of edible oil. There has been a decrease of 70% of the gossypol content in seed (as it is a toxic polyphenolic compound) and 92% decreased in the accumulation of foliar gossypol due to the engineering of cotton plants with antisense G. arboreum δ-(+) cadinene synthase ( cdn1-Cl ) gene under the control of the promoter CaMV35S (Martin et al. 2003 ).

Gene editing in cotton

Recently, the CRISPR/Cas9 system has emerged as an effective technique to modify a gene in both plants and animals. It is based on the immune response of prokaryotes against foreign nucleic acid and viruses and has been successfully deployed in eukaryotes for targeted genome modifications ( Horvath and Barrangou 2010 ; Koonin and Makarova 2009 ). Earlier developed systems such as mutagenesis and gene targeting like zinc finger nuclease and TALEN are already in use, but the efficiency of CRISPR is much higher and target-oriented. The CRISPR/ Cas9 consists of two components: the first one is a guide RNA (gRNA) that finds the sequence which is targeted in the genome and the second one is a nuclease that breaks DNA sequence which is targeted at a specific location. There are almost 20 nucleotide sequences that are complementary to the target sequence in a single gRNA and the other tracrRNA:crRNA which forms a hairpin structure and binds with the nuclease portion in Cas9. This sgRNA/Cas9 complex allows cleavage at site in the target genome with greater precision (Mali et al. 2013 ). The ease of use of CRISPR/Cas9 system has helped to achieve a lot within limited resources.

Polyploidy crops, e.g., the upland cotton as a tetraploid, are always difficult for genome editing as they have multiple sets of chromosomes and a higher number of alleles. But recent studies have shown significant success in targeted mutagenesis and genome editing of G. hirsutum . (Li et al. 2017 ). As an allotetraploid crop, the cotton genome is very complex (2n = 4x = 52) with a very large genome size, i.e. 2.5 Gb (Li et al. 2019 ). Many genes have multiple copies in cotton. Different gene editing strategies have been adopted in cotton which include CRISPR/Cpf1 (Cas12a), CRISPR/LbCpf1, CRISPR/Cas9 and multiplex systems of CRISPR, Zhang et al. ( 2018 ) have reported simultaneous editing of two copies of Gh14–3-3d genes in upland cotton.

It has become quite easy to utilize molecular tools due to the availability of the genome sequence of cotton. They can be used to to evaluate the function of genes and improve the agronomic characters by targeting specific genes for better performance and quality traits (Li et al., 2015 ; Zhang et al. 2016 ). Despite limitations, there have been reports of successful gene editing for G. barbadense and G. hirsutum which show allotetraploid behavior with double the number of targets compared with that in diploid crops. For example, Li et al. ( 2017 ) have successfully reported the gene-editing of cotton. The CRISPR/Cas9 system has been known as a broad method to control various geminiviruses in Pakistan. However, this method only targets single virus and it has not been found beneficial to control complexes of a begomovirus associated with DNA-satellites. In addition, a cassette of sgRNA is made to target not only complete CLCuD-associated begomovirus complexes (Iqbal et al. 2016 ). Although, CRISPR Cas/9 has made the genome editing quite simple and efficient; it can result in non-specific editing due to a mismatch in the gRNA sequence (Ahmad et al. 2020 ). The genome-editing technology is still under investigation for its limitations such as off-targets, low mutagenesis efficiency, persisted CRISPR activity in subsequent generations, risk of instability of edited genome, scarcity of validated targets and its dependency on in-vitro regeneration protocols for the recovery of stable plant lines (Ahmad et al. 2020 ).

Marker-assisted selection (MAS) status in Pakistan

In plant breeding, the selection of plants in a segregating population with the desired characteristics and suitable gene combinations is an important component. Marker-assisted selection is the selection of phenotypes based on the genotype of markers (Collard et al. 2005 ). The MAS is the most important in breeding programs because it improves the effectiveness and productivity of breeding methods over conventional breeding. To facilitate quantitative agronomic traits, researchers utilize mapping of QTLs and MAS. The RAPD markers have been extensively applied for MAS to obtain glandless seed and glanded plant in an interspecific population (Mergeai et al. 1998 ). A breeder can easily identify the plant which carries the gene if the markers are strongly associated with the targeted gene (Young 1996 ). DNA markers associated with important QTLs such as qtl FS1 for fiber strength are useful in F 2 generation of varieties cultivated on large scale (Zhang et al. 2003 ) . The cotton varieties NIBGE-115 and NIBGE-2 were developed by combining the conventional and the genomic tools to develop the resistance against cotton curl leaf disease at National Institute of Biotechnology and Genetic Engineering (NIBGE) (Rahman and Zafar, 2007a , b ). In 2009, Mumtaz et al. obtained two CLCuV resistant cotton genotypes, namely CIM-443 and CIM-240 through marker-assisted screening in Pakistan. The collected data regarding the above genotypes suggested that they can be used in future cotton breeding. The cost and effectiveness of MAS depend on the selection of marker technology. Thus, it should be selected with considerable care during crop improvement.

Since the start of the cotton biotechnology program, several key steps and strategies have been adopted for crop improvement at different research institutions in Pakistan. Of these, conservation of germplasm resources, genetic engineering and transformation technologies, molecular markers-assisted selection, classical breeding programs, improvement of fiber quality, better resistance against biotic and abiotic stresses, policy-making for knowledge dissemination and variety approval process are important factors that have been taken into consideration. Pakistan still lags behind in yield per area compared with other major cotton-growing countries. Further researches on adaptation of genetic engineering technologies, academic industry linkage and future policy-making are required for the improvement of crops and to deal with future challenges.

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Abbas A, Iqbal MA, Rahman MU, et al. Estimating genetic diversity among selected cotton genotypes and the identification of DNA markers associated with resistance to cotton leaf curl disease. Turk J Bot. 2015;39(6):1033–41 https://doi.org/10.3906/bot-1505-22 .

Article   CAS   Google Scholar  

Abdalla AM, Reddy OUK, El-Zik KM, et al. Genetic diversity and relationships of diploid and tetraploid cottons revealed using AFLP. Theor Appl Genet. 2001;102(2–3):222–9 https://doi.org/10.1007/s001220051639 .

Abdellatif KF, Khidr YA, El-Mansy YM, et al. Molecular diversity of Egyptian cotton ( Gossypium barbadense L.) and its relation to varietal development. J Crop Sci Biotechnol. 2012;15(2):93–9.

Article   Google Scholar  

Abdullah A. An analysis of Bt cotton cultivation in Punjab, Pakistan using the agriculture decision support system (ADSS). AgBioForum. 2010;13(3):274–87.

Google Scholar  

Abdurakhmonov IY. Exploiting genetic diversity 1. Adv Genet. 2007;23:272–5.

Abdurakhmonov IY, Kohel RJ, Yu JZ, et al. Molecular diversity and association mapping of fiber quality traits in exotic G. hirsutum L. germplasm. Genom. 2008;92(6):478–87.

Abdurakhmonov IY, Saha S, Jenkins JN, et al. Linkage disequilibrium-based association mapping of fiber quality traits in G. hirsutum L. variety germplasm. Gene. 2009;136(3):401–17.

Agarwal M, Shrivastava N, Padh H. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep. 2008;27(4):617–31.

Article   CAS   PubMed   Google Scholar  

Ahmad N, Rahman MU, Mukhtar Z, et al. A critical look on CRISPR-based genome editing in plants. J Cell Physiol. 2020;235(2):666–82 https://doi.org/10.1002/jcp.29052 .

Ahmad S, Zhang T, Shaheen T, et al. Identifying genetic variation in Gossypium based on single nucleotide polymorphism. Pak J Bot. 2007;39(4):1245.

Ali A, Ahmed S, Nasir IA, et al. Evaluation of two cotton varieties CRSP1 and CRSP2 for genetic transformation efficiency, expression of transgenes Cry1Ac + Cry2A , GT gene and insect mortality. Biotech Rep. 2016;1(9):66–73 https://doi.org/10.1016/j.btre.2016.01.001 .

Ali I, Kausar A, Rahman M, et al. Development of genetic linkage map of leaf hairiness in Gossypium hirsutum (cotton) using molecular markers. Pak J Bot. 2009;41(4):1627–35.

CAS   Google Scholar  

Alvarez I, Wendel JF. Cryptic interspecific introgression and genetic differentiation within Gossypium aridum (Malvaceae) and its relatives. Evo. 2006;60(3):505–17.

Awan MF, Abass MA, Muzaffar A, et al. Transformation of insect and herbicide resistance genes in cotton ( Gossypium hirsutum L.). J Agric Sci Technol. 2015;17(2):287–98.

Bajwa KS, Shahid AA, Rao AQ, et al. Expression of Calotropis procera expansin gene CpEXPA3 enhances cotton fibre strength. Aus J Crop Sci. 2013;7(2):206.

Bakhsh A, Rao AQ, Shahid AA, et al. Insect resistance and risk assessment studies in advance lines of Bt cotton harboring Cry1Ac and Cry2A genes. Am Eurasian J Agric Environ Sci. 2009;6(1):1–11.

Bayley C, Trolinder N, Ray C, et al. Engineering 2, 4-D resistance into cotton. Theor Appl Genet. 1992;83(5):645–9.

Brubaker CL, Paterson AH, Wendel JF. Comparative genetic mapping of allotetraploid cotton and its diploid progenitors. Genome. 1999;42(2):184–203.

Campbell BT, Saha S, Percy R, et al. Status of the global cotton germplasm resources. Crop Sci. 2010;50(4):1161–79.

Chen G, Du XM. Genetic diversity of source germplasm of upland cotton in China as determined by SSR marker analysis. Acta Genetica Sinica(Yi Chuan Xue Bao). 2006;33(8):733–45.

Chen Y, Wang Y, Wang K, et al. Construction of a complete set of alien chromosome addition lines from Gossypium australe in Gossypium hirsutum : morphological, cytological, and genotypic characterization. Theor Appl Genet. 2014;127(5):1105–21 https://doi.org/10.1007/s00122-014-2283-1 .

Article   PubMed   PubMed Central   Google Scholar  

Chen Z, Gallie DR. The ascorbic acid redox state controls guard cell signaling and stomatal movement. Plant Cell. 2004;16(5):1143–62.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Collard BC, Jahufer MZZ, Brouwer JB, et al. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica. 2005;142(1–2):169–96.

Crickmore N, Zeigler DR, Feitelson J, et al. Revision of the nomenclature for the Bacillus thuringiensis pesticidal crystal proteins. Microbiol Mol Biol Rev. 1998;62(3):807–13.

Dai S, Zheng P, Marmey P, et al. Comparative analysis of transgenic rice plants obtained by Agrobacterium -mediated transformation and particle bombardment. Mol Breed. 2001;7(1):25–33.

De Block M. The cell biology of plant transformation: current state, problems, prospects and the implications for the plant breeding. Euphytica. 1993;71:1–2):1–14.

Depicker A, Van Montagu M. Post-transcriptional gene silencing in plants. Curr Opin Cell Biol. 1997;9(3):373–82.

Article   PubMed   Google Scholar  

Dill GM, CaJacob CA, Padgette SR. Glyphosate-resistant crops: adoption, use and future considerations. Pest Manag Sci. 2008;64(4):326–31.

Diouf L, Magwanga RO, Gong W, et al. QTL mapping of fiber quality and yield-related traits in an intra-specific upland cotton using genotype by sequencing (GBS). Int J Mol Sci. 2018;19(2):441 https://doi.org/10.3390/ijms19020441 .

Article   PubMed Central   CAS   Google Scholar  

Dongre AB, Raut MP, Paikrao, et al. Genetic purity testing of cotton F1 hybrid DHH-11 and parents revealed by molecular markers. Intl Res J Biotech. 2012;3(3):32–6.

Fang DD, Xiao J, Canci PC, et al. A new SNP haplotype associated with blue disease resistance gene in cotton ( Gossypium hirsutum L.). Theo App Genet. 2010;120(5):943–53.

Food and Agriculture Organization of United Nations (FAO) 2019. http://www.fao.org/news/archive/news-by-Dec/2019/en/ . Accessed 28 June 2020.

Gould J, Banister S, Hasegawa O, et al. Regeneration of Gossypium hirsutum and Gossypium . barbadense from shoot apex tissues for transformation. Plant Cell Rep. 1991;10(1):12–6.

Guo X, Huang C, Jin S, et al. Agrobacterium -mediated transformation of Cry1C , Cry2A and Cry9C genes into Gossypium hirsutum and plant regeneration. Biol Plant. 2007;51(2):242–8.

Horvath P, Barrangou R. CRISPR/Cas, the immune system of bacteria and archaea. Sci. 2010;327(5962):167–70.

Hou WS, Guo SD, Lu M. Development of transgenic wheat with a synthetical insecticidal crystal protein gene via pollen-tube pathway. Acta Agron Sin. 2003;29(6):806–9.

Huang G, Dong Y, Sun J. Introduction of exogenous DNA into cotton via the pollen-tube pathway with GFP as a reporter. Chin Sci Bull. 1999;44(8):698.

Hutchinson JB. New evidence on the origin of the old-world cotton. Heridity. 1954;8:225–41.

Iqbal MA, Rahman MU. Identification of marker-trait associations for lint traits in cotton. Front Plant Sci. 2017;8:86 https://doi.org/10.3389/fpls.2017.00086 .

Iqbal MJ, Reddy OU, El-Zik KM, et al. A genetic bottleneck in the ‘evolution under domestication ‘of upland cotton Gossypium hirsutum L. examined using DNA fingerprinting. Theo App Genet. 2001;103(4):547–54.

Iqbal Z, Sattar MN, Shafiq M. CRISPR/Cas9: a tool to circumscribe cotton leaf curl disease. Front Plant Sci. 2016;7:475 https://doi.org/10.3389/fpls.2016.00475 .

Islam MS, Thyssen GN, Jenkins JN, et al. A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton. BMC Genomics. 2016;17(1):903 https://doi.org/10.1186/s12864-016-3249-2 .

Article   PubMed   PubMed Central   CAS   Google Scholar  

John ME, Keller G. Metabolic pathway engineering in cotton: biosynthesis of polyhydroxybutyrate in fiber cells. Proc Natl Acad Sci. 1996;93(23):12768–73.

Kalia RK, Rai MK, Kalia S, et al. Microsatellite markers: an overview of the recent progress in plants. Euphytica. 2011;177(3):309–34 https://doi.org/10.1007/s10681-010-0286-9 .

Kalivas A, Xanthopoulos F, Kehagia O, et al. Agronomic characterization, genetic diversity and association analysis of cotton cultivars using simple sequence repeat molecular markers. Genet Mol Res. 2011;10(1):208–17.

Kantartzi SK, Ulloa M, Sacks E, et al. Assessing genetic diversity in Gossypium arboreum L. cultivars using genomic and EST-derived microsatellites. Genetica. 2009;136(1):141–7.

Khan AI, Awan FS, Sadia B, et al. Genetic diversity studies among colored cotton genotypes by using RAPD markers. Pak J Bot. 2010;42(1):71–7.

Khan SA, Hussain D, Askari E, et al. Molecular phylogeny of Gossypium species by DNA fingerprinting. Theo App Genet. 2000;101(5-6):931–8.

Kiani S, Ali A, Bajwa KS, et al. Cloning and chloroplast-targeted expression studies of insect-resistant gene with ricin fusion-gene under chloroplast transit peptide in cotton. Electron J Biotechnol. 2013;16(6):13 https://doi.org/10.2225/vol16-issue6-fulltext-2 .

Kohel RJ, Yu J, Park YH, et al. Molecular mapping and characterization of traits controlling fiber quality in cotton. Euphytica. 2001;121(2):163–72.

Koonin EV, Makarova KS. CRISPR-Cas: an adaptive immunity system in prokaryotes. F1000 Bio Reports. 2009;1:95. https://doi.org/10.3410/B1-95 .

Kumar S, Dhingra A, Daniell H. Stable transformation of the cotton plastid genome and maternal inheritance of transgenes. Plant Mol Biol. 2004;56(2):203–16.

Lacape JM, Dessauw D, Rajab M, et al. Microsatellite diversity in tetraploid Gossypium germplasm: assembling a highly informative genotyping set of cotton SSRs. Mol Breed. 2007;19(1):45–58.

Lee M. DNA markers and plant breeding programs. In: Sparks DL, editor. Adv in Agron, vol. 55. Newark: Academic press; 1995. p. 265–344.

Li B, Rui H, Li Y, et al. Robust CRISPR/Cpf1 (Cas12a)-mediated genome editing in allotetraploid cotton ( Gossypium hirsutum ). Plant Biotechnol J. 2019;17(10):1862–4 https://doi.org/10.1111/pbi.13147 .

Li C, Unver T, Zhang B. A high-efficiency CRISPR/Cas9 system for targeted mutagenesis in cotton ( Gossypium hirsutum L.). Sci Rep. 2017;7:43902 https://doi.org/10.1038/srep43902 .

Li F, Fan G, Lu C, et al. Genome sequence of cultivated upland cotton ( Gossypium hirsutum TM-1) provides insights into genome evolution. Nat Biotechnol. 2015;33(5):524–30 https://doi.org/10.1038/nbt.3208 .

Article   PubMed   CAS   Google Scholar  

Liu D, Guo X, Lin Z, et al. Genetic diversity of Asian cotton ( Gossypium arboreum L.) in China evaluated by microsatellite analysis. Genet Resour Crop Evol. 2006;53(6):1145–52.

Liu S, Cantrell RG, McCarty JC, et al. Simple sequence repeat–based assessment of genetic diversity in cotton race stock accessions. Crop Sci. 2000;40(5):1459–69.

Lokhande S, Reddy KR. Quantifying temperature effects on cotton reproductive efficiency and fiber quality. Agron J. 2014;106(4):1275–82 https://doi.org/10.2134/agronj13.0531 .

Lu H, Myers G. Genetic relationships and discrimination of ten influential upland cotton varieties using RAPD markers. Theo Appl Genet. 2002;105(2–3):325–31.

Lu Y, Wei G, Zhu Y. Cloning whole cellulose-synthesizing operon (ayacs operon) from Acetobacter xylinum and transforming it into cultivated cotton plants. Acta Bot Sin. 2002;44(4):441–4.

Mali P, Esvelt K, Church G. Cas9 as a versatile tool for engineering biology. Nat Methods. 2013;10:957–63 https://doi.org/10.1038/nmeth.2649 .

Mao YB, Tao XY, Xue XY, et al. Cotton plants expressing CYP6AE14 double-stranded RNA show enhanced resistance to bollworms. Transgenic Res. 2011;20(3):665–73 https://doi.org/10.1007/s11248-010-9450-1 .

Martin GS, Liu J, Benedict CR, et al. Reduced levels of cadinane sesquiterpenoids in cotton plants expressing antisense (+)-δ-cadinene synthase. Phytochem. 2003;62(1):31–8.

Masood A, Arif MJ, Hamed M, et al. Field performance of Trichogramma chilonis against cotton bollworms infestation in different cotton varieties as a sustainable IPM approach. Pak J Agric Agric Eng Vet Sci. 2011;27(2):176–84.

McCabe DE, Martinell BJ. Transformation of elite cotton cultivars via particle bombardment of meristems. Biotech. 1993;11(5):596–8.

Mehboob-ur-Rahman YT, Tabbasam N, et al. Studying the extent of genetic diversity among Gossypium arboreum L. genotypes/cultivars using DNA fingerprinting. Genet Resour Crop Evol. 2008;55:331–9.

Mergeai G, Baudoin JP, Vroh BI. Production of high-gossypol cotton plants with low-gossypol seed from trispecific hybrids including Gossypium sturtianumWillis. In: Proceedings of the World Cotton Res Con. Unité de Phytotechnie des Régions Intertropicales, Faculté Universitaire des Sciences Agronomiques , B5030, vol. 2. Belgium: Gembloux; 1998. p. 206–1.

Mumtaz AS, Naveed M, Shinwari ZK. Assessment of genetic diversity and germination pattern in selected cotton genotypes of Pakistan. Pak J Bot. 2010;42(6):3949–56.

Muzaffar A, Kiani S, Khan MAU, et al. Chloroplast localization of Cry1Ac and Cry2A protein-an alternative way of insect control in cotton. Biol Res. 2015;48(1):1–11.

Myles S, Peiffer J, Brown PJ, et al. Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell. 2009;21(8):2194–202.

Nida DL, Kolacz KH, Buehler RE, et al. Glyphosate-tolerant cotton: genetic characterization and protein expression. J Agric Food Chem. 1996;44(7):1960–6.

Noormohammadi Z, Al-Rubaye MTS, Sheidai M, et al. ISSR, RAPD and agronomic study in some F1 and F2 cotton genotypes. Acta Biol Szegediensis. 2011;55(2):219–25.

Noormohammadi Z, Farahani YH, Sheidai M, et al. Genetic diversity analysis in Opal cotton hybrids based on SSR, ISSR, and RAPD markers. Genet Mol Res. 2013a;12(1):256–69.

Noormohammadi Z, Farahani YHA, Sheidai M, et al. Methodology genetic diversity analysis in Opal cotton hybrids based on SSR, ISSR, and RAPD markers. Genet Mol Res. 2013b;12(1):256–69.

Nordborg M, Tavaré S. Linkage disequilibrium: what history has to tell us. Trends Genet. 2002;18(2):83–90.

Ordon F, Wenzel W, Friedt W. Recombination: molecular markers for resistance genes in major grain crops. In: Behnke HD, Esser K, Kadereit JW, Lüttge U, Runge M, editors. Progress in Botany, vol. 59. Berlin, Heidelberg: Springer; 1998. https://doi.org/10.1007/978-3-642-80446-5_2 .

Chapter   Google Scholar  

Pillay M, Myers GO. Genetic diversity in cotton assessed by variation in ribosomal RNA genes and AFLP markers. Crop Sci. 1999;39(6):1881–6.

Prioul JL, Quarrie S, Causse M. Dissecting complex physiological functions through the use of molecular quantitative genetics. J Exp Bot. 1997;48(6):1151–63.

Qayyum A, Murtaza N, Azhar FM, et al. Biodiversity and nature of gene action for oil and protein contents in Gossypium hirsutum L. estimated by SSR markers. J Food Agri Environ. 2009;7(2):590–3.

Rahman M, Hussain D, Zafar Y. Estimation of genetic divergence among elite cotton cultivars–genotypes by DNA fingerprinting technology. Crop Sci. 2002;42(6):2137–44.

Rahman M, Zafar Y. Registartion of NIBGE-115 cotton- development of cotton and wheat cultivars by bridging conventional and genomic tools. J Plant Reg. 2007a;1(1):51–2 https://doi.org/10.3198/jpr2006.12.0778crg .

Rahman M, Zafar Y. Registration of ‘NIBGE-2’ cotton- development of cotton and wheat cultivars by bridging conventional and genomic tools. J Plant Reg. 2007b;1(2):113–4 https://doi.org/10.3198/jpr2007.02.0071crc .

Rahman MU, Khan AQ, Rahmat Z, et al. Genetics and genomics of cotton leaf curl disease, its viral causal agents and whitefly vector: a way forward to sustain cotton fiber security. Front Plant Sci. 2017;8:1157 https://doi.org/10.3389/fpls.2017.01157 .

Rahman MU, Shaheen T, Tabbasam N, et al. Cotton genetic resources. A review. Agron Sustain Dev. 2012;32:419–32 https://doi.org/10.1007/s13593-011-0051-z .

Rana MK, Singh VP, Bhat KV. Assessment of genetic diversity in upland cotton ( Gossypium hirsutum L.) breeding lines by using amplified fragment length polymorphism (AFLP) markers and morphological characteristics. Genet Resour Crop Evol. 2005;52(8):989–97.

Rao AQ, Ali MA, Khan MAU, et al. Science behind cotton transformation. In: Abdurakhmonov IY, editor. Cotton ressearch. London: IntechOpen; 2016. https://doi.org/10.5772/64888 . Accessed 24 May 2020.

Rashid B, Saleem Z, Husnain T, et al. Transformation and inheritance of Bt genes in Gossypium hirsutum . J Plant Bio. 2008;51(4):248–54 https://doi.org/10.1007/BF03036123 .

Reddy MP, Sarla N, Siddiq EA. Inter simple sequence repeat (ISSR) polymorphism and its application in plant breeding. Euphytica. 2002;128(1):9–17 https://doi.org/10.1023/A:1020691618797 .

Rehman A, Jingdong L, Chandio AA, et al. Economic perspectives of cotton crop in Pakistan: a time series analysis (1970–2015) (part 1). J Saudi Soc Agric Sci. 2019;18(1):49–54 https://doi.org/10.1016/j.jssas.2016.12.005 .

Ribaut JM, Hoisington D. Marker-assisted selection: new tools and strategies. Trends Plant Sci. 1998;3(6):236–9.

Robinson AF, Bell AA, Dighe ND, et al. Introgression of resistance to nematode Rotylenchulus reniformis into upland cotton ( Gossypium hirsutum ) from Gossypium longicalyx . Crop Sci. 2007;47(5):1865–77.

Saeed M, Guo WZ, Zhang TZ. Association mapping for salinity tolerance in cotton ( Gossypium hirsutum L.) germplasm from US and diverse regions of China. Aus J Crop Sci. 2014;8(3):338–46.

Saleem MA, Amjid MW, Ahmad MQ, et al. Marker assisted selection for relative water content, excised leaf water loss and cell membrane stability in cotton. Adv Life Sci. 2018;5(2):56–60.

Saleem MA, Malik TA, Shakeel A, et al. QTL mapping for some important drought tolerant traits in upland cotton. J Animal Plant Sci. 2015;25:502–9.

Shang L, Liang Q, Wang Y, et al. Identification of stable QTLs controlling fiber traits properties in multi-environment using recombinant inbred lines in upland cotton ( Gossypium hirsutum L.). Euphytica. 2015;205(3):877–88 https://doi.org/10.1007/s10681-015-1434-z .

Shen X, Guo W, Lu Q, et al. Genetic mapping of quantitative trait loci for fiber quality and yield trait by RIL approach in upland cotton. Euphytica. 2007;155(3):371–80 https://doi.org/10.1007/s10681-006-9338-6 .

Siebert MW, Nolting S, Leonard BR, et al. Efficacy of transgenic cotton expressing Cry1Ac and Cry1F insecticidal protein against Heliothines (Lepidoptera: Noctuidae). J Econ Entomol. 2008;101(6):1950–9.

Singh P, Kumar M, Chaturvedi C, et al. Development of a hybrid delta-endotoxin and its expression in tobacco and cotton for control of a polyphagous pest Spodoptera litura . Transgenic Res. 2004;13(5):397–410.

Sunilkumar G, Campbell LM, Puckhaber L, et al. Engineering cottonseed for use in human nutrition by tissue-specific reduction of toxic gossypol. Proc Natl Acad Sci. 2006;103(48):18054–9.

Tan Z, Fang X, Tang S, et al. Genetic map and QTL controlling fiber quality traits in upland cotton ( Gossypium hirsutum L.). Euphytica. 2015;203(3):615–28 https://doi.org/10.1007/s10681-014-1288-9 .

Tarazi R, Jimenez JL, Vaslin MF. Biotechnological solutions for major cotton ( Gossypium hirsutum ) pathogens and pests. Biotech Res Innov. 2020;3:19–26 https://doi.org/10.1016/j.biori.2020.01.001 .

Tatineni V, Cantrell RG, Davis DD. Genetic diversity in elite cotton germplasm determined by morphological characteristics and RAPDs. Crop Sci. 1996;36(1):186–92.

Tyagi P, Gore MA, Bowman DT, et al. Genetic diversity and population structure in the US Upland cotton ( Gossypium hirsutum L.). Theo App Genet. 2014;127(2):283–95.

Ullah I, Iram A, Iqbal MZ, et al. Genetic diversity analysis of Bt cotton genotypes in Pakistan using simple sequence repeat markers. Genet Mol Res. 2012;11(1):597–605.

Van Deynze A, Stoffel K, Lee M, et al. Sampling nucleotide diversity in cotton. BMC Plant Biol. 2009;9(1):125.

Wang XQ, Feng CH, Lin ZX, et al. Genetic diversity of sea-island cotton ( Gossypium barbadense ) revealed by mapped SSRs. Genet Mol Res. 2011;10(4):3620–31.

Wendel JF, Cronn RC. Polyploidy and the evolutionary history of cotton. Adv Agron. 2003;78:139.

Wendel JF, Rowley R, Stewart JM. Genetic diversity in and phylogenetic relationships of the Brazilian endemic cotton, Gossypium mustelinum (Malvaceae). Plant Syst Evol. 1994;192(1–2):49–59.

Westengen OT, Huaman Z, Heun M. Genetic diversity and geographic pattern in early South American cotton domestication. Theo App Genet. 2005;110(2):392–402.

Wilkins TA, Rajasekaran K, Anderson DM. Cotton biotechnology. Crit Rev Plant Sci. 2000;19(6):511–50.

Wu J, Luo X, Guo H, et al. Transgenic cotton, expressing Amaranthus caudatus agglutinin, confers enhanced resistance to aphids. Plant Breed. 2006;125(4):390–4.

Xu QH, Zhang XL, Nie YC. Genetic diversity evaluation of cultivars ( G. hirsutum L.) from the Changjiang river valley and Yellow river valley by RAPD markers. Yi Chuan Xue Bao (Acta Genetic Sinica). 2001;28(7):683–90.

Yin K, Han T, Xie K, et al. Engineer complete resistance to Cotton Leaf Curl Multan virus by the CRISPR/Cas9 system in Nicotiana benthamiana . Phytopathol Res. 2019;1:9 https://doi.org/10.1186/s42483-019-0017-7 .

Young ND. QTL mapping and quantitative disease resistance in plants. Annu Rev Phytopathol. 1996;34(1):479–501.

Zapata C, Park SH, El-Zik KM, et al. Transformation of a Texas cotton cultivar by using agrobacterium and the shoot apex. Theor Appl Genet. 1999;98(2):252–6 https://doi.org/10.1007/s001220051065 .

Zhang HB, Li Y, Wang B, Chee PW. Recent advances in cotton genomics. Int J Plant Genomics. 2008;2008(742304):20. https://doi.org/10.1155/2008/742304 .

Zhang JF, Wu M, Yu J, et al. Breeding potential of introgression lines developed from interspecific crossing between upland cotton ( Gossypium hirsutum L.) and Gossypium barbadense: heterosis, combining ability and genetic effects. PLoS One. 2016;11(1):e0143646 https://doi.org/10.1371/journal.pone.0143646 .

Zhang Y, Wang XF, Li ZK, et al. Assessing genetic diversity of cotton cultivars using genomic and newly developed expressed sequence tagderived microsatellite markers. Genet Mol Res. 2011;10:1462–70.

Zhang Z, Ge X, Luo X, et al. Simultaneous editing of two copies of Gh14-3-3d confers enhanced transgene-clean plant defense against Verticillium dahliae in allotetraploid upland cotton. Front Plant Sci. 2018;9:842 https://doi.org/10.3389/fpls.2018.00842 .

Zhang ZS, Li XB, Xiao YH, et al. Combining ability and heterosis between high strength lines and transgenic Bt ( Bacillus thuringiensis ) bollworm-resistant lines in upland cotton ( Gossypium hirsutum L.). Agric Sci China. 2003;2(1):13–8.

Zhao FY, Li YF, Xu P. Agrobacterium -mediated transformation of cotton ( Gossypium hirsutum L. cv. Zhongmian 35) using glyphosate as a selectable marker. Biotech Letters. 2006;28(15):1199.

Zhou GY, Weng J, Zeng Y, et al. Introduction of exogenous DNA into cotton embryos. Methods Enzymol. 1983;101:433–81.

Zhu J, Chen J, Gao F, et al. Rapid mapping and cloning of the virescent-1 gene in cotton by bulked segregant analysis–next generation sequencing and virus-induced gene silencing strategies. J Exp Bot. 2017;68(15):4125–35 https://doi.org/10.1093/jxb/erx240 .

Zhu LF, Zhang XL, Nie YC. Analysis of genetic diversity in upland cotton ( Gossypium hirsutum L.) cultivars. J Agric Biotech. 2003;11(5):450–5.

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Acknowledgements

We are thankful to Professor Ashraf M, Vice Chancellor, University of Agriculture Faisalabad on providing us some cotton germplasm data of Pakistan.

Conflict of interest

Authors declare that they have no conflict of interest for the publication of the manuscript.

This work was funded by the Cooperative Innovation Project of Agricultural Science and Technology Innovation Program of CAAS (CAAS-XTCX2018020–15).

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RAZZAQ Abdul, ZAFAR Muhammad Mubashar, HAFEEZ Abdul, SHI Yuzhen, GONG Wankui & YUAN Youlu

Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan

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FB Genetics Four Brothers Group, Lahore, Pakistan

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Razzaq A and Zafar MM wrote the initial draft of the manuscript. Ali A, Hafeez A and Batool W made all necessary corrections. Shi YZ carried out final editing of manuscript. Gong WK proofed read the manuscript. Final approval for publication was given by the group leader at institute of cotton research Yuan YL. The author(s) read and approved the final manuscript.

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Correspondence to RAZZAQ Abdul or YUAN Youlu .

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RAZZAQ, A., ZAFAR, M.M., ALI, A. et al. Cotton germplasm improvement and progress in Pakistan. J Cotton Res 4 , 1 (2021). https://doi.org/10.1186/s42397-020-00077-x

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DOI : https://doi.org/10.1186/s42397-020-00077-x

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July 6, 2021

Babar Latif Baloch, Toby Penrhys-Evans, Umair Safdar

Organic cotton in Pakistan: policy analysis and recommendations

As the fourth largest cotton producer worldwide, cotton is an integral part of Pakistan’s economy contributing 9.5% of its gross domestic and serving as a core livelihood for 15 million cotton workers. In addition to bringing US$3.5 billion as foreign currency each year to the country, it provides crucial income to cotton-producing households, accounting for nearly, 40% of the household income for landowners, and almost 45% of the household income for sharecroppers (tenant farmers who give a part of each crop for rent).

However, the cotton crop in Pakistan is under threat. Insect-pest attack, climate change and acute water shortages have placed immense pressures on farmers, and there has been a growing appeal for farmers to grow other cash crops instead, such as sugarcane.

With these problems affecting cotton farmers in Pakistan, some believe that organic cotton is a more sustainable option. With increasing impacts of climate change, avoiding fossil fuel-based fertilisers and saving precious water resources by growing in areas with naturally high rainfall reduces the impact on the environment. It also reduces the use of hazardous synthetic pesticides, and allows farmers to safely grow food crops next to their cotton.

Rising organic cotton demands from the USA, Europe, the UK, and other high-end apparel markets have also sent a strong message to Pakistan and other developing countries, that if they are able to make changes at policy and field level they will be able to reap significant financial rewards.

However, certified organic cotton accounts for under 1% of global cotton cultivation. In Pakistan, a lack of policies, unavailability of pure non-genetically modified organism (GMO) seed and ineffective links with input suppliers and supply chains creates barriers between farmers and the opportunity to grow organic cotton, reducing interest in the crop.

Under the Cotton Advocacy for Policy and Seed (CAPAS) project, funded by the Laudes Foundation , CABI and 25 cotton stakeholders (including the Ministry of Agriculture and local government ministries, research institutes and farmer organisations, and others) conducted a National Organic Cotton Policy GAP Analysis focused on Balochistan, the first province to harvest organic cotton in Pakistan. They looked at gaps in existing policies, documents, and regulatory frameworks, and suggested new policies needed to scale-up organic cotton production.

A working paper on the subject has now been published . The analysis found that producing profitable organic cotton does interest farmers, but that they need more government support in the provision of an organic agriculture loan facility, increased availability of bio-inputs and provision of an organic cotton premium. The analysis came up with the following recommendations:

1. Creation of a seed multiplication system

A lack of access to organic cotton seed is an important barrier that is restricting farmers from growing organic cotton. To counter this problem, organic cotton seed multiplication programmes need to be established to increase the access of organic seed varieties to farmers. There should also be a focus on immediate, medium-term, and long-term approaches that also engage research institutes, seed companies, the Federal Seed Certification and Registration Department (FSC&RD) and farming communities.

2. Seed quality assurance

Contamination of seed samples in research institutes, where GMO cotton seeds were discovered among non-GMO seeds, was found to be another major problem. To combat this, there needs to be a seed quality assurance system implemented along the whole cotton seed supply chain to ensure that non-GMO seed is available. This is not possible without engaging with organic cotton farming communities to identify their needs and look for viable ways forward.

3. National certification and laboratory testing system

There are a number of scenarios where accidental contamination between GMO and non-GMO seeds could occur at any point along the supply chain such as when the seeds are in transit, or cross-contamination from pesticides from neighbouring non-organic farms. To address this, free laboratory testing facilities for cotton seed and organic cotton samples of farmer’s fields should be established, along with a national organic certification system for organic cotton textile products.

4. Private sector engagement

For the organic cotton supply chain to flourish, the private sector needs to be fully on board to help all elements of the fledgling industry to emerge. Recent amendments to Pakistan’s laws have enabled private seed companies to develop their own organic cotton variants with more freedom and legal security to protect the development of new biotechnology. The opportunity to engage with these new investors should not be missed.

5. Access to a credit facility

Farmers seeking a loan from commercial banks are restricted by legal formalities, while a recent baseline study of CABI’s CAPAS project showed that more than 90% of farmers are illiterate and so struggle to deal with bank formalities to access agricultural loans. In addition, the State Bank of Pakistan (SBP), as the central bank of Pakistan, has regulations that are conducive for all commercial banks operating in the country regarding how they offer agriculture loans to farming communities. By setting up a credit facility, the SBP could create an enabling environment for organic cotton farmers, facilitating finance for options such as agricultural machinery which could help support the long-term sustainability of their businesses.

6. Capacity building programme

Seed certification in Pakistan is mainly the responsibility of FSC&RD, which suffers from a shortage of staff and lacks capacity for seed testing. The FSC&RD, alongside other cotton institutes in the country engaged in cotton research and development, need to create capacity building programmes for organic cotton seed development and the production of biological inputs.

Way forward

There are clearly many obstacles to the development of a sustainable organic cotton infrastructure in Pakistan, but CABI’s analysis found that Pakistan can benefit from an increasing demand for organic cotton if firm interventions are put in place. The government, on its part, is keen to implement a long-term plan to boost organic cotton production, establish an organic textile supply chain, develop high-yielding seed varieties, and enable farmers to get good quality inputs, credit and fair prices for their produce. A strong desire among organic cotton farmers and a firm commitment from the government to invest and engage could be enough to give the industry the kickstart that needs to become a sustainable and long-term option for farmers in Pakistan.

Additional information

Main image : From left to right Lakshmi, Mangi and Bharti sort cotton on a farm during a harvest in village Khudabad Chandia, UC Hala, district Matiari of Sind province in Pakistan (Credit: ©️Asim Hafeez for CABI)

To read the working paper in full, visit Pakistan National Organic Cotton Policy GAP Analysis .

For more information about the connected project to this working paper, and the partners involved, visit ‘ Promoting sustainable organic cotton production and supply in Pakistan .’

This organic cotton blog is a concise yet comprehensive guide to eco-friendly fashion. I appreciate how it emphasizes the importance of sustainable choices and their impact on our planet. It’s motivating me to make more environmentally conscious decisions when shopping for clothes.

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Factors Influencing Sustainable Production of Cotton in Pakistan: A Case Study From Bahawalpur District

2014, International Journal of Sciences Basic and Applied Research

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The present study was designed to explore the important factors that affecting cotton production such as socioeconomic conditions of cotton growers which affect the yield directly .The data on various cost items including land, labour and capital inputs, marketing cost and physical and revenue productivity, net return, input-output ratio and cost benefit ratios and farm sizes during the year 2018-2019.By using multi-stages cluster sampling survey method .The poor production implies implication that the illiteracy ,ignorance, Inadequate canal water, insect pest and poor extension services soil quality implications of various inputs like cultivation, seed and sowing irrigation inter-culturing-hoeing, fertilizer, plant protection and labour cost on cotton yield could be the causes to this low production due to lack of marketing facilities at village level, less payment by the marketing agencies ,high prices of input ,lack of timely availability of genuine fertilizers. The practical results indicated that significant increase in output of cotton in the study area could be traced mainly to use to latest technology that plays the vital role in cotton productivity enhancement.

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The present study was designed to explore the important actors that affecting cotton production such as socio-economic condition of cotton growers which affect the yield directly. The data on various cost items including land, labour, and capital inputs, marketing costs and physical and revenue productivity, net return, input-output ratio and cost-benefit ratios and farm sizes during the year 2010-11, were collected from 60 selected cotton farmers for this purpose, from different villages of district Naushahro Feroze by using multi-stages cluster sampling survey method. The poor production implies implications that the, illiteracy, ignorance, inadequate canal water, insect pest and poor extension services soil quality implications of various inputs like cultivation, seed and sowing, irrigation, inter-culturing / hoeing ,fertilizer, plant protection, and labour cost on cotton yield could be the causes to this low production due to lack of marketing facilities at village level, less payment by the marketing agencies, high prices of inputs, lack of timely availability of genuine fertilizers. The practical results indicated that significant increase in output of cotton in the study area could be traced mainly to use of latest technology that plays the vital role in cotton productivity enhancement.

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rummana zaheer

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Southern Punjab of Pakistan known as the cotton zone, study was conducted to examine cost benefit analysis of cotton cultivation in district Muzaffargarh (core cotton zone), Punjab in 2015-16. The focus of the study was to evaluate economic analysis of cotton production and financial impact of cotton growers in cotton cultivation. A sample of one hundred cotton growers were randomly selected and directly interviewed for pre-tested questionnaire. Benefit cost ratio estimated 1.479 which denoted the profitability of cotton cultivation. Econometric model of cotton profit function was examined, price of output and quantity produced of cotton positively affect profit, while cost of production negatively affect profit. It was determined Cropped Area, Land Preparation, Seed, Fertilizer, Pesticides, Irrigation and Labor statically significant and positively affects cotton production. Proper policies are pertinent to input prices and output of cotton mandatory for cotton growers to increase profitability and refining socioeconomic status in farming community.

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The present study was designed to determine the factors affecting cotton productivity in Punjab province of Pakistan. The factors considered in the study are livestock assets in addition to other conventional factors including farm inputs and socioeconomic characteristics. A Cobb Douglas production function was estimated. Impacts of livestock assets on various farm characteristics like share of Bt cotton, cotton area and dummy for good quality of land are considered. Findings of the study show that variables namely pesticide, irrigation, farming experience, cotton area, dummy for good quality land, dummy for off-farm income, dummy for livestock units, interaction terms of livestock units with pesticide and ratio of Bt cotton area to area under cotton are significantly related with cotton yield. Combined effect of livestock and pesticide use on cotton productivity is 0.38 percent whereas joint contribution of livestock and share of Bt cotton in cotton yield is 0.01 percent. Integrati...

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Unpacking Climate Impacts and Vulnerabilities of Cotton Farmers in Pakistan: A Case Study of Two Semi-arid Districts

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• Volume 2 , pages 499–514, ( 2018 )

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This paper aims to contribute to the understanding of climate risks and vulnerability facing cotton farmers in semi-arid regions of Pakistan. Given the ever-increasing climate change impacts on cotton production in Pakistan, especially in semi-arid regions where water scarcity puts additional pressure on water sensitive agricultural livelihoods, we have conducted this study to identify climate risks facing cotton farmers in two semi-arid districts of Punjab province (average annual contribution to total cotton production is 80%), based on various climate indicators (such as temperature, rainfall and climate extremes.). A mix of qualitative and quantitative methods has been used to explore factors of vulnerability and comparative vulnerabilities. In the cotton production stage, we found that vulnerability to climate change decreases with increase in the size of the landholding, mainly because large farmers have more financial resources at their disposal to deal with adverse climate impacts, such as crop damages and losses. Adaptive capacity, on the other hand, is found to be one of the significant factors determining the overall vulnerability at the household level, as level of exposure and sensitivity do not differ across different sized households. In other words, indicators of adaptive capacity, such as access to financial resources, diversified livelihoods and access to weather information plays a major role in reducing vulnerability against climate change.

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1 Introduction

Climate change is exacerbating the existing challenges of the agriculture sector (IPCC 2014 ), especially for agro-based economies, such as Pakistan, which has severe implications for productivity, livelihoods and economic growth (Siddique et al. 2012 ). Agriculture is the backbone of Pakistan’s economy. It accounts for 19.5% of the gross domestic product (GDP), employs 42.3% of the total labour force and provides raw material for several value-added sectors ((GoP) Government of Pakistan 2017 ). Within agriculture, cotton is one of the major sectors driving economic growth of the country. It contributes 5.2% to the agricultural value addition and has a share of 1.0% in the GDP of Pakistan ((GoP) Government of Pakistan 2017 ). While cotton production declined significantly during 2015–2016, primarily due to flood (a major impact of climate change), the policy focus has shifted to agriculture and especially to the cotton sector. This is also in recognition that climate change is projected to exacerbate risks in the future, which would have serious implications for the export economy of Pakistan (Batool and Saeed 2017 ). Moreover, textile industry, a major industrial sector of the country, is largely dependent on the cotton crop for production and hence any adverse impact on local cotton crop has implications for textile sector (in the form of reduced input supply) and in turn, for the national economy (Batool and Saeed 2017 ).

Studies based on the station observations found an increasing trend of temperature in Pakistan (Río et al. 2013 ; Sheikh et al. ( 2009 ); ADB ( 2017 ); Sadiq and Qureshi ( 2010 ); Khattak and Ali 2015 ; Ali et al. 2016 ). Moreover, future projections based on climate modelling also suggest a continuation of this trend (Batool and Saeed 2017 ; Saeed and Athar 2017 ). Besides mean temperature, climate extremes such as heat waves also show an increasing trend both in the past and in future projection for Pakistan (Maida and Rasul  2010 ; Saeed et al. 2017 ). Furthermore, studies also point towards an increase in temporal and spatial variability of precipitation both in the past as well as future (Ali et al. 2016 ; Ikram et al. 2016 ; Salma et al. 2012 ; Hussain and Lee ( 2014 ); Siddique et al. 2012 ). This has consequences not only in terms of droughts and dry spells, but combine effect of snow/ice melting and heavy monsoon precipitation makes the country very vulnerable to flooding events. As a result of these climate impacts, there are a huge number of reported evidences of increased poverty, food insecurity and health deterioration (Asian Development Bank 2017 ). Not only this, livestock, forestry, water, energy, transport and agriculture sector in particular have found to be adversely affected by climate change to a large extent (Asian Development Bank 2017 ). This in turn, have huge implications for the livelihoods of millions of people associated with these sectors and their value chains Footnote 1 (Batool and Saeed 2017 ).

Cotton is considered to be a highly sensitive crop and climate change (both gradual change and extreme events) is expected to have profound impacts on its productivity (Ton 2011 ). Cotton crop is particularly vulnerable to high temperatures, CO 2 concentration in the atmosphere, low water availability, high atmospheric evaporation rate and heat stress (MacKerron 2001 ; Richardson et al. 2002 ; Singh et al. 2007 ; Bange 2007 ). A comparative analysis of climate impact on cotton crop across several countries, including China, Pakistan, Australia, the US and Brazil among others, indicates that a likely decrease in rainfall along with rising temperatures will likely cause the cotton’s demand for water to increase in all of the study areas (Ton 2011 ). While the pace of glacial depletion is getting faster, there is a likelihood that water in the Indus River (shared by Pakistan, India and China), which is a major source of irrigation for nearby cotton producing regions, would start to run dry by the end of this century (Kakakhel 2015 ).

These studies exclusively highlight direct impacts of climate change on crop production (including productivity and yield) and do not take into account impacts on livelihoods of the farmers. However, given huge macro-economic implications of climate change and agricultural production being an entry point for climate impacts, it is important to dive deep into the micro-level impacts to identify and address key climate risks. In addition to understanding future climate risks, vulnerability assessment is the key to build resilience through targeted interventions which can help to reduce identified vulnerabilities. While every region has faced with varying degrees and types of climate vulnerabilities, depending on geographical, social, economic and cultural features, a one-size-fits-all solution cannot work (Hinkel 2011 ). Hence, there arises a need for vulnerability assessments that are specific to location and sector that can guide practitioners and policy makers to devise and implement targeted policy actions (Panthi et al. 2016 ).

Limited literature exists on farm level livelihood vulnerabilities in Pakistan. Rehman et al. ( 2018 ) hint towards deteriorating incomes as a result of changing climate. Food security vulnerability arising out of climate change is briefly highlighted in Ullah ( 2017 ). CIAT and World Bank ( 2017 ) also highlight infrastructure damages and employment losses in the aftermath of climate extreme events. Asian Development Bank ( 2017 ) also found significantly negative impact of changing climate on the livestock, which is one of the major sources of livelihood for farmers in Pakistan. Apart from these, a major concern of the farmers now is climate impacts on crop production. Crop production in Pakistan is projected to reduce by 8–10% by 2040 as a result of increase in temperature (Dehlavi et al. 2015 ). In terms of adaptation, Abid et al. ( 2015 ) found that adaptation practices at local level pertains to simple measures such as changing of crop sowing date, as opposed to advance management technologies.

In this backdrop, this study focuses on understanding the climate risks and vulnerabilities facing cotton farmers in Pakistan. The findings of this paper can help generate evidence on how cotton farmers in semi-arid regions are affected by climate impacts, and more importantly, how social differentiation determines the level of vulnerability between different landholders and locations. Enhanced understanding on these issues would be beneficial in designing targeted policy interventions for building resilience of cotton farmers in the face of ever-increasing climate risks. The targeted focus on semi-arid regions is because of the fact that Pakistan’s water resources are under great threat of climate change, especially in semi-arid regions (Salik et al. 2015 ).

The paper is organized as follow; Sect.  2 details the methodology adopted for this study, Sect.  3 presents extensive discussion on results and Sect.  4 concludes the paper with policy recommendations for enhancing resilience of cotton farmers, to climate risks.

2 Data and methodology

This research paper follows the mixed methods research approach that integrates both qualitative and quantitative tools to explore research questions in depth. The broader aim of this paper is to understand and explore climate risks facing cotton farmers for promoting climate-resilient livelihoods and economic development. In line with this, the paper will address the following two research questions:

What are the current and future climate change risks at the cotton production stage?

What are some of the factors of differential vulnerability across various socio-economic groups and locale?

We started our analysis with identification of future climate risks for cotton production in Pakistan and particularly for study sites, using a global gridded crop model called EPIC (Environmental Policy Integrated Climate Model) (Williams et al. 1989 ) which is an agro-ecosystem model running with a daily time step. It simulates crop development and yield, hydrological, nutrient and carbon cycles and a wide range of crop management activities. It takes inputs of minimum and maximum temperature (°C), precipitation (mm), global radiation (MJ m −2 ), CO 2 concentration, different soil properties, crop heat requirement. In addition, it also takes different parameters related to soil properties and crop management as input. The data used for this paper was produced as a part of ISIMIP (Inter-Sectoral Impact Model Inter-comparison Project) initiative (Warszawski et al. 2014 ).

For these simulations, underlying assumptions include the use of Representative Concentration Pathway (RCP) 8.5 scenario, the use of Shared Socio-Economic Pathway (SSP) 2 scenario, the representation of full irrigation, and the representation of CO 2 fertilization. In ISIMIP, all crop models (including EPIC) are forced with only five Global Climate Models (GCMs) which are HadGEM2-ES, MIROC-ESM-CHEM, IPSL-CM5A-LR, GFDL-ESM-2 M and NorESM1-M. Hence, the present analysis is based on the ensemble of these five climate models which is presented by taking the relative difference of four future period (2016–2035, 2036–2055, 2056–2075, 2076–2095) against historical period of 1981–2000. Spatially averaged future yields are also presented for the provinces of Punjab and Sindh which together accounts for more than 95% of country’s cotton production. The data from a global gridded crop model EPIC, forced with five different global climate models (GCMs) runs is obtained from ISIMIP (Inter-Sectoral Impact Model Inter-comparison Project) database (Warszawski et al. 2014 ). For this simulation, underlying assumptions include the use of Representative Concentration Pathway (RCP) 8.5 scenario, the use of Shared Socio-Economic Pathway (SSP) 2 scenario, the representation of full irrigation, and the representation of CO 2 fertilization. In earlier literature, RCP 8.5 and SSP2 are both referred as ‘business as usual’ and ‘middle of the road’, respectively (Fricko et al. 2017 ). Moreover, among the four IPCC AR5 scenarios, RCP 8.5 is the highest emission scenario.

For farm level climate impacts information, we carried out an extensive survey of 436 farming households (cotton farmers) in two semi-arid districts of Punjab province (see full map in “Appendix 1 ”). The questionnaire was composed of close-ended questions related to household (household members, education, gender ratio) and farm (irrigation pattern, type of crops, level of production, climate impacts) level information. Key Informant Interviews (KIIs) and Focused Group Discussions (FGDs) were also done to gain further insights into the vulnerability at community level. Dera Ghazi Khan (DGK) and Faisalabad (FSD) were selected as study sites, based on their high contribution to the total cotton production of the country (42% of the cotton production in semi-arid regions in Punjab comes from these two districts). Three union councils (UC) were selected in DGK (namely, Kala, Mana Ahmadani and Mor Jhangi) and one in FSD (namely 91), based on their cotton production averages in the last 10 years. The selection of UCs was carried out to capture the diversity in climate risks and vulnerabilities within the study sites, with DGK UCs (Kala, Mana Ahmadani, Mor Jhangi) being sensitive to hill torrent and riverine flooding, while FSD UC (91) to heat extremes. The detailed description of the UCs is presented in “Appendix 2 ”.

A sampling framework was developed in collaboration with the local (district level) agriculture department. A full list of cotton farmers (140–160 per UC) was developed for each UC and then a minimum of 100 randomly selected farmers were interviewed in each UC. To ensure that the survey captured different land tenure systems as well, the following categorization was made for each UC: landless farmers (tenants, Footnote 2 sharecroppers Footnote 3 and contractors Footnote 4 ) small farmers (holding less than 12 acres of land), medium farmers (holding more than 12 acres but less than 25 acres) and large farmers (holding more than 25 acres). Footnote 5 A minimum of 75 landholders Footnote 6 and 25 in each UC were interviewed. The category-based data allowed us to make comparisons across different landholdings and location.

Table  1 further elaborates the sample size as per each category of farmers. While DGK has a greater share of cotton production than FSD, 75% of the sample was selected from DGK whereas FSD accounted for 25% of the sample.

2.1 Calculation of vulnerability index

Both top-down (e.g. climate modelling-based approaches) and bottom up approaches (focus on what causes communities to be vulnerable) exist to study climate vulnerabilities (Hinkel et al. 2014 ; Dessai and Hulme 2004 ; Van Aalst et al. 2008 ). Although a combination of both would be an ideal situation to identify vulnerabilities, lack of site-specific climate data allowed us to opt a bottom-up approach to identify climate vulnerabilities facing cotton farmers. One of the major advantage of using a bottom-up approach is that it helps in the identification of vulnerable groups and differences in vulnerabilities (even at small spatial scale) (Hinkel et al. 2014 ).

Moreover, to carry out vulnerability assessment, we followed the vulnerability framework defined in IPCC AR4 i.e.

Vulnerability = ƒ (exposure, sensitivity, adaptive capacity)

This implies that vulnerability of an individual or household is directly proportional to exposure and sensitivity, whereas it is inversely proportional to adaptive capacity. This relationship has been endorsed by various experts including Adger ( 2006 ); Weis et al. 2016 ; Metzger and Schröter ( 2006 ), etc. Exposure in particular, is related to the changes in climatic parameters (their intensity and frequency) and its potential impacts on resources. It answers the question of ‘what is exposed?’ and refers to the presence of people, livelihoods, species or ecosystems, environmental services and resources, infrastructure, or economic, social, or cultural assets in places that could be adversely affected (IPCC 2014 ). On the other hand, sensitivity is more dependent on socio-economic factors (gender, decision making power, mobility options, community structure, etc.), that may or may not reduce the adverse impacts of climate change (Cardona et al. 2012 ). Adaptive capacity plays a positive role in decreasing vulnerability against climate threat through adjustments in current behaviours. Some major attributes of adaptation include education level, networks (that promotes social learning and knowledge exchange), access to economic resources, livelihood diversification, social support institutions, etc. (Weis et al. 2016 ).

After selecting the proxy variables for Exposure, Sensitivity and Adaptive capacity (“Appendix 4 ”), we normalised the variables based on the functional relationship of variables with vulnerability, using min–max normalisation (Iyengar and Sudarshan 1982 ). In case of a positive relation (for example vulnerability vs. exposure and sensitive capacity), Eq.  1 was used, whereas Eq.  2 was used in case of negative relationship (for example, vulnerability vs. adaptive capacity).

where \(X_{ij}\) denotes the value of indicator j ( j  = 1,2,3… n ) in the i village ( i  = 1,2,3… n ) and \(Y_{ij}\) is the normalised score. The normalised values lie between 0 and 1.

In the next step, equal weights were assigned to each indicator using simple average of normalised score and vulnerability index was obtained using Eq.  3 :

where \(X_{ij}\) , \(Y_{ij}\) and \(Z_{ij}\) are indicators used as proxy variables for sensitivity, exposure and adaptive capacity. The vulnerability score obtained tells us the comparative vulnerabilities across various households. Using this methodology, we have derived comparative vulnerabilities:

Based on UCs

Based on landholding

To obtain a score based on each UC or landholding, we aggregated the sum of individuals in a particular UC or landholding. The final score for each component explains the level of vulnerability of people residing in a particular UC. UCs and Landholders were then ranked based on the vulnerability score. First rank represents extreme vulnerability, whereas vulnerability decreases with the increase in rank (5th rank = lowest vulnerability). The value of each component (Exposure, sensitivity and adaptive capacity) ranges between 0 and 1, where 1 means most vulnerable and 0 means least vulnerable.

Finally, in terms of limitations, this paper focuses on two districts only and the findings cannot be generalized for Punjab or Pakistan. The findings, however, are reflective of the overall situation in similar regions.

3 Results and discussion

3.1 future cotton production trends.

Cotton production provides a direct entry point for climate impacts, which then trickle down to the associated value chains (Batool and Saeed 2017 ). We have developed the impact modelling for the cotton crop to see how cotton yield might change over time under a changing climate.

Figure  1 depicts future changes in the cotton yield over Pakistan relative to the base period 1981–2000, by taking the average of the whole ensemble (EPIC forced with 5 GCMs), for four different time slabs. As mentioned earlier, RCP 8.5 is the most extreme scenario in the suite of scenarios developed for IPCC AR5. We based our results on this scenario because it is also called as business-as-usual scenarios. The negative impacts of climate change on cotton yield start to appear right from the first-time slab (2016–2035) in northern region of Punjab. However, it is important to note that there is a slight increase in the central to southern Punjab region (Blue area) which is mainly attributed to the effects of CO 2 fertilization (McGrath and Lobell 2013 ), which tends to have a positive effect on crop yield (depending on crop type and other factors). However, a consistent decrease in the cotton yield is witnessed in other time slabs towards the end of the century. In the last time slab from 2076 to 2095, an acute reduction of around 60–80.0% can be seen in most of the cotton producing areas of Punjab and Sindh, especially in Punjab, which accounts for 80.0% cotton production of the country.

Mean projected relative changes (in  %) in cotton yield relative to 1981–2000 for Pakistan using global gridded crop model EPIC forced by HadGEM2-ES, MIROC-ESM-CHEM, IPSL-CM5A-LR, GFDL-ESM-2M, NorESM1-M

As mentioned in Sect.  2 , the purpose of analysing the data from global crop model is to have an idea about the future impact of global warming on the yield of cotton crop at the national level in future. Since the provinces of Punjab and Sindh together accounts for more than 95% of the country’s cotton production, hence we show future yield of cotton averaged over these two province in Fig.  2 as relative difference (in %) from the base period. These are plotted by taking the spatial average of annual cotton yield over the provinces of Punjab and Sindh. A moving average of 10 years has been applied to smoothen the data by removing year-to-year variability. The analysis is carried out by taking the relative difference of four future period (2016–2035, 2036–2055, 2056–2075, 2076–2095) against historical period of 1981–2000. The dark lines represent median of the ensemble, whereas the shaded area represents the full range of the ensemble.

Future change in cotton yield in Punjab calculated as relative difference (in %) from the annual averaged value of historical period (1981–2000)

The figure projects a sharp decline in cotton yield by the end of this century, in a business as usual scenario, in which no significant change in mitigation or adaptation practices is made. The cotton yield is projected to decline by at least 60.0% by 2096, which will not only affect the livelihood of the cotton farmers but the impacts will likely have a trickle-down effect on the associated industries as well as the overall economy of Pakistan (Batool and Saeed 2017 ).

3.2 Vulnerability assessment at farm level

As mentioned earlier, we adopted IPCC AR4 vulnerability framework which is presented in Sect.  2 . This section briefly outlines the indicators used to calculate exposure, sensitivity and adaptive capacity of the landholders and landless cotton farmers.

3.2.1 Exposure

Pakistan is severely exposed to climate risks as evident from climate events in the past and future threats identified in the literature (Asian Development Bank 2017 ). This section discusses the climate indicators used to derive the level of exposure of different landholders to climate change in our study sites.

3.2.1.1 Frequency of climate events

As a measure of exposure to climate risks, we have analysed perception-based data (due to lack of downscaled climate data for each district under consideration) on the occurrence of climate extreme events in our study sites.

Table  2 provides information about the frequency and intensity of various primary and secondary climate events experienced by cotton farmers in DGK and FSD. At the overall sample scale, heat wave, Footnote 7 monsoon variability, resultant pest attack and price shocks after these events are witnessed at a higher intensity and scale relative to other climate-related shocks. 51.4 and 56.4% of the respondents said that they experienced heat wave and monsoon variability, respectively, between 1 and 3 times during the last 10 years. Similarly, 42.0% of the respondents witnessed pest attacks more than 3 times during the last 10 years.

The data also highlights that a large proportion of respondents said that they have never experienced floods, droughts, erosion, waterlogging, etc. which provides a rationale to develop policy interventions to safeguard these farmers, who are currently not under the threat of floods and droughts, as they could be faced with such challenges with climate change in coming decades. Moreover, there is a large percentage of cotton farmers who said that they have faced pest attack, erosion, heat wave and floods more than 1 time and up to 10 times during the last 10 years.

Geographic location is a major factor determining the exposure to climate change. DGK is highly vulnerable to flood risk due to close proximity to River Indus but FSD is not exposed to any such threat. Even within DGK, proximity of communities to Indus River defines the level of exposure to floods. Moreover, some communities reported being particularly vulnerable to hill-torrent flood risk, depending on their location. Within our sample size, 22.0% of the respondents were solely affected by riverine floods while 26.2% were affected by hill torrents. 10.1% of the respondents reported to have been affected by both hill torrents and riverine floods.

Based on the data, we find that overall floods, drought, monsoon variability and heat wave (as primary indicator, leading to other issues) are the major issues faced by cotton farmers. Analysis of location based climate risks highlights that UC Kala and Mor Jhangi are more exposed to floods as compared to UC 91 and Mana Ahmadani (Table  3 ). These UCs have issues pertaining to heat wave and rainfall variability.

3.2.2 Sensitivity

Some of the factors determining the sensitivity of the cotton farmers in DGK and FSD are discussed below.

3.2.2.1 Number of male and female labourers involved in agricultural activities

A large number of females are involved in agriculture in DGK and FSD. Women bring girls with them for cotton picking and the picking activity serves as a networking platform for women at village level. Number of female labourers employed (at the respondents’ land) for farm activities is almost double than male labourers in case of UC 91 and Mana Ahmadani.

3.2.2.2 Percentage of farmers (respondents) affected by climate events

Figure  3 below highlights percentage of sampled cotton farmers affected by various climate events in different UCs in DGK and FSD during the last 10 years. In the case of Kala, 95.7% of the respondents said that they were affected by pest attack and floods in the last 10 years, followed by heat wave, prices shock and monsoon variability. Similar climate risks have been faced by UC Mana Ahmadani. UC Mor Jhangi is more affected by floods (almost 99.1% of the respondents affected by flood), price shocks and monsoon variability. While no episode of flood has been recorded in FSD, UC 91 is more affected from pest attack (99.0%) and heat wave (80.8%). Heat wave is one of the many reasons for pest attacks in Pakistan (Zulfiqar et al. 2010 ; Ton 2011 ; Baig and Amjad 2014 ).

Percentage of farmers reported to be affected by different climate change indicators during the last 10 years

If we segregate our data on the basis of UCs and climate indicators, we find that there is a large proportion of cotton farmers that perceive high sensitivity to climate hazards. For example, in Table  4 , we have clustered all those farmers who have witnessed the climate event at least once in the last 10 years under ‘sensitive to climate change. We have also developed another category of farmers who have experienced various climate events more than 3 times during the last 10 years and labelled them as ‘severely sensitive’. Then based on these, we have categorised the percentage of population of farmers exposed. Results are presented in the form of a sensitivity matrix where green represents the population exposure of less than 30.0%, yellow represents population affected is greater than 30.0% but less than 50.0%, and red represents more than 50.0% of affected population.

According to these measures, in UC 91 44.9 and 35.5% of the farmers are characterized as severely sensitive to risk of drought and heat wave, respectively. Conversely, farmers in UC Kala appear to be severely sensitive to floods (53.0%), heat wave (33.9%) and monsoon variability (20.0%). Largely, 95.0% of the cotton farmers said that they are sensitive to flood hazard and 88.7% of the farmers are sensitive to heat wave. In Mana Ahmadani, severe sensitivity to heat wave (36.4% of the farmers) and drought (21.9% of the farmers) can be found. A large proportion of farmers in Mor Jhangi (99.0%) reported that they are sensitive to flood risk while out of these, 69.2% are severely sensitive to the same. Heat wave and monsoon variability is also one of the severe risks facing farmers in Mor Jhangi.

3.2.2.3 Percentage of population dependent on canal water for irrigation

In our sample size, we found that 45% of the farmers use only tube well water for irrigation whereas 50% of the farmers use both canal and tube well water. Due to variability in canal water availability, only around 5% of the farmers rely solely on canal water for irrigation. Segregation of data as per UC shows that Mana Ahmadani and 91 have the largest number of farmers who also use canal water for irrigation.

3.2.3 Adaptive capacity

A multitude of studies concludes that adaptive capacity plays a major role in reducing vulnerabilities and building resilience to climate impacts. As indicated earlier, access to education and resources (both physical and financial), livelihood diversification and social networks that promotes knowledge exchange are some of the key attributes of adaptation (Weis et al. 2016 ; Mendoza et al. 2014 ). Using these as indicators, we have built an indicator of adaptive capacity to compare differential adaptive capacities among sampled farmers and UCs.

3.2.3.1 Education

In terms of education of the respondents (cotton farmers), UC Mor Jhangi and 91 has the largest number of college graduate cotton farmers (around 30–40%). On the other hand, UC 91 has a highest percentage (38%) of uneducated cotton farmers, followed by UC Kala (48%).

3.2.3.2 Livelihood diversification

Within our sample, agriculture is the primary source of income for 96% of the households. However, 57.3% of the households have a major second source of income. Out of those, 21.8% of the households rely on livestock for livelihood after agriculture. Other major secondary sources of income include government job (7.6%), construction (6.2%) and shop keeping (4.1%). At UC level, UC Kala has the least number of farmers dependent on only a single source of income, i.e. farming. On the other hand, UC Mana Ahmadani and Mor Jhangi has the most percentage of farmers with diversified income sources.

Similarly, small and large landholders are more inclined towards income diversification, according to our sample. There is, however, no clear trend in case of landless farmers.

3.2.3.3 Wealth status

To derive sensitivities based on wealth status, we have calculated the wealth index. The methodology has been explained in “Appendix 3 ”. Wealth index divides our respondents into five categories, i.e. very rich, rich, middle, poor and very poor. Landless farmers are categorised into very poor and poor categories. Small and medium farmers are represented in poor, middle and rich category whereas large landholders are mostly categorised into very rich category (see “Appendix 3 ”).

As we are interested to compare wealth differences across UCs, we find that UC Kala and Mana Ahmadani have the largest percentage of very poor and poor cotton farmers (Fig.  4 ). However, Kala also has the largest number of rich cotton farmers, followed by UC More Jhangi. In terms of sensitivity, interventions should be targeted at more vulnerable farmer’s groups, i.e. very poor and poor.

Percentage of farmers as per wealth categories/UCs

3.2.3.4 Access to financial services and post-disaster compensation

Out of a sample of 436 farmers, only 34.4% have a bank account. 39.7% of farmers said that they have access to crop loans and insurance. However, almost 18.0% of the farmers reported that they do not prefer to take loans despite having access to financial services. Major reasons cited for not taking loans is high interest rates on borrowing and low capacity to return loans with interest. A small proportion (1.0%) of the respondents also reported religious reasons for not taking loans.

The level of compensation after flood events for each UC is directly proportional to the severity of the flood. For example, a high number of farmers received compensation after 2010 floods in More Jhangi (90.0%) and Kala (52.2%) as compared to farmers in Mana Ahmadani (13.2%).

3.2.3.5 Access to weather information

With regards to weather information, 81.7% of the total respondents reported that they receive weekly or monthly updates on weather. There is a statistically significant relationship (correlation is significant at the 0.05 level; p value 0.021) between land size and receipt of weather information such that the number of farmers who receive monthly/weekly updates increase with the increase in the size of land. For example, 79.0% of small farmers said they receive weather updates as opposed to 85.5 and 92.2% of medium and large farmers.

3.2.3.6 Early warning

In our sample, only 12.4% of the farmers from flood-affected areas reported that they had received warning prior to the 2010 flood. Among those who reported to have been warned, 83.6% said that the warning included information about the severity of flood. While UC Kala and Mor Jhangi had severe episodes of flooding in 2010, 32.2% of the respondents from Kala and 16.3% from Mor Jhangi reported that they did not receive a warning before the flood.

3.3 Vulnerability Index

Since the main objectives of this paper is to see how climate vulnerabilities differ across various groups of farmers and UCs. In this section, we will analyse if there are any differences in vulnerability to climate change among different groups of landholders (small, medium and large) as well as farmers across different UCs. The relationship used for the calculation of vulnerability Index is presented in ‘Data and Methodology’ section. The proxy variables used to calculate vulnerability index are discussed in detail in previous sections. Functional relationship of these variables with elements of vulnerability is defended in “Appendix 3 ”. Appendix 4 explains the steps used to derive the vulnerability index.

3.3.1 Comparative vulnerabilities across different landholdings

Figure  5 summarises the overall results of the vulnerability index. The value of each component ranges between 0 to 1, where 1 means most vulnerable and 0 means least vulnerable. The figure shows that landless farmers are most vulnerable to climate change, followed by the categories of small landholders, medium landholders and large landholders. The vulnerability of landholders is related to the level of adaptive capacity as provided by access to financial services, strong networks which allows them to gain knowledge on new and adaptive agricultural practices, etc. Larger landholders are more likely to have access to these sources of adaptive capacity whereas small landholders and landless farmers are less likely to have large social networks, access to credit, or other assets such as livestock.

Results of the vulnerability index (comparative bar chart)

Based on the components of vulnerability, we also find that there is less variation in exposure and sensitivity to climate change between landholders and landless. On the other hand, large differences in adaptation were found among both these groups which suggests that adaptation capacity shapes vulnerability to climate change in the case of landholders and landless cotton farmers in semi-arid regions of Pakistan. While exposure and sensitivity to climate change are partly determined by external factors such as household dependency ratio and number of climate events experienced at farm level, adaptation decision-making can be promoted through targeted policy interventions that build institutional capacities and promote knowledge creation and sharing.

3.3.2 Comparative vulnerabilities across UCs

We find that farmers from UC Kala are the most vulnerable to climate change, followed by Mana Ahmadani, 91 and Mor Jhangi. A higher level of vulnerability implies high exposure and sensitivity to climate change coupled with low levels of adaptive capacity to cope with climate change.

UC Kala is highly exposed to flood risks and has a relatively low percentage of farmers with livelihood diversification (45.0%), has larger households (having up to 20 members) and the highest number of non-educated cotton farmers. Similarly, Mana Ahmadani is also highly vulnerable to several climate change, including monsoon variability, drought and heat wave that lead to pest attacks. A major percentage of the farmers (82.7%) are dependent on canal water for irrigation which makes Mana Ahmadani highly sensitive to climate change. UC 91 has almost the same characteristics as Mana Ahmadani and is particularly vulnerable to heat stress. But since the level of exposure and sensitivity is not as high, this UC has a relatively lower vulnerability score.

On the other hand, farmers in Mor Jhangi, who are the most affected in terms of floods (as shown by exposure) is found to be least vulnerable as compared to other UCs. This is due primarily to the relatively high adaptive capacity of the farmers in this UC as Mor Jhangi has the highest number of college graduate farmers. It also has the highest rate of livelihood diversification primarily because of high risk of flood every year due to which people do not rely solely on agricultural income.

4 Conclusion and way forward for promoting climate resilient cotton production in Pakistan

The findings of this paper provide crucial policy entry points, which can help build resilience of the cotton farmers in Pakistan that are under continued threat of climate change. Cotton sector has suffered huge losses as a result of adverse impacts of climate in the past few years and will continue to be affected by large extent, as depicted by our climate projections, if adaptation measures are not taken.

While it is crucial to understand underlying features of vulnerability to promote adaptation, we find that vulnerability to climate change decreases with increased size of landholding. More importantly, wealth plays an important part in promoting adaptation decisions at household level but it does not always ensure adaptation decisions. In other words, not all wealth farmers adapt to climate impacts. In terms of policy, it implies that there is a need for farmer level awareness raising about climate change and its implications for agricultural productivity. Currently, there is no formal mechanism to disperse climate information top down, where it is needed the most. Information is disseminated through social networks, which benefit large landholders with large social networks. This information gap regarding current climate risks and adaptation requirements results in major losses in productivity and limited adaptation to future climate risks.

Second, despite varying level of vulnerability across landholdings, we find that all landless and landholders are vulnerable to climate change. Flood being a recurrent climate risk for Pakistan ends in loss of livelihoods of millions of farmers across the country. This not only results in massive food insecurity but also increase in poverty as medium and small landholders are further pushed down the poverty line as they fail to recover from flood damages. Crop insurance, a potential tool to deal with financial losses, is although available (through public banks) but have extremely limited outreach. In this context, crop insurance tools need to be developed that caters for both short and long-term climate related losses faced by farmers. Targeted insurance tools should be developed for landless daily wage laborers.

Finally, data collected on the access to weather information also suggest extremely limited access of farmers to weather and climate information services. Weather information infrastructure at the local level should be upgraded and efforts should be made to develop easy access of farmers to information data. Again, training of farmers on how to interpret and utilize climate data for effective adaptation is required at the local level.

A value chain is a cumulative process through which a product gains value at each step before reaching end users.

A person who occupies a land rented from a landlord.

A tenant farmer who gives a part of each crop as rent.

A daily wage labourer working for landholder under a seasonal contract.

The categorization is done based on the official categories of different farmers done by the Pakistan Bureau of Statistics.

It is also important to note that classification of farmers was done on the basis of total landownership and not on cotton cultivation area as it is difficult to find farmers cultivating cotton on more than 25 acres of land.

Heat wave refers to prolonged period of excessive heat along with high humidity.

Abid M, Scheffran J, Schneider UA, Ashfaq M (2015) Farmers’ perceptions of and adaptation strategies to climate change and their determinants: the case of Punjab province, Pakistan. Earth Syst Dyn 6:225–243

Article   Google Scholar  

Adger N (2006) Vulnerability. Global Environ Change 16(2006):268–281 (Elsevier)

Ali S, Khattak M, Khan D, Sharif M, Khan H, Ullah A, Malik A (2016) Predicting future temperature and precipitation over Pakistan in the 21st century. J Eng Appl Sci (JEAS) 35:61–76 (University of Engineering and Technology, Peshawar)

Google Scholar  

Asian Development Bank (2017) Climate change profile of Pakistan. Report. Manila, Philippines

Baig MA, Amjad S (2014) Climate change impact on major crops of Pakistan: a forecast for 2020, Institute of Business Management

Bange M (2007) Effects of climate change on cotton growth and development. The Australian Cotton Grower. June issue. Green Mount Press, Australia

Batool S, Saeed F (2017) Mapping the cotton value chain in Pakistan: a preliminary assessment for identification of climate vulnerabilities & pathways to adaptation. PRISE working paper. Sustainable Development Policy Institute, Islamabad

Cardona OD, van Aalst MK, Birkmann J, Fordham M, McGregor G, Perez R, Pulwarty RS, Schipper ELF, Singh BT (2012) Determinants of risk: exposure and vulnerability. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) Managing the risks of extreme events and disasters to advance climate change adaptation A special report of working Groups I and II of the intergovernmental panel on climate change (IPCC). Cambridge University Press, Cambridge, pp 65–108

CIAT and World Bank (2017) Climate-Smart Agriculture in Pakistan. CSA Country Profiles for Asia Series. International Center for Tropical Agriculture (CIAT); The World Bank. Washington, D.C., p 28

Dehlavi A, Groom B, Gorst A (2015) Climate change adaptation in the indus ecoregion: a microeconometric study of the determinants, impacts, and cost effectiveness of adaptation strategies. World Wide Fund for Nature (WWF) Pakistan, Islamabad

Dessai S, Hulme D (2004) Does climate adaptation policy need probabilities? Clim Policy 4:107–128

Devkota JU, Singh S, Prajapati C, Hada B (2014) Principal components analysis of the socioeconomic conditions of biogas users - with examples from Nepal. Int J Renew Energy Res 4(3):655–664

Filmer D, Pritchett LH (2001) Estimating wealth effects without expenditure data or tear: an application to educational enrolments in states of India. Demography 38(1):115–132

Fricko O, Havlik P, Rogelj J, Klimont Z, Gusti M, Johnson N, Kolp P, Strubegger M, Valin H, Amann M, Ermolieva T, Forsell N, Herrero M, Heyes C, Kindermann G, Krey V, McCollum DL, Obersteiner M, Pachauri S, Rao S, Schmid E, Schoepp W, Riahi K (2017) The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century. Global Environ Change 42:251–267

(GoP) Government of Pakistan (2017) Economic survey of Pakistan 2016–2017. Ministry of Finance

Hinkel J (2011) Indicators of vulnerability and adaptive capacity: towards a clarification of the science policy interface. Global Environ Change 21:198–208

Hinkel J, Schipper L, Wolf S (2014) A Framework for Climate Change Vulnerability Assessments. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, India

Hussain MS, Lee S (2014) Long-term variability and changes of the precipitation regime in Pakistan. Asia Pac J Atmos Sci 50(3):271–282

Ikram F, Afzaal M, Bukhari S, Ahmed B (2016) Past and future trends in frequency of heavy rainfall events over Pakistan. Pakistan J Meteorol 12(24):57–78

IPCC (2014) Climate Change 2014: impacts, adaptation, and vulnerability Part B: regional aspects. In: Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 688

Iyengar NS, Sudarshan P (1982) A method of classifying regions from multivariate data. Econ Polit Wkly 17:2048–2052

Kakakhel S (2015) The Indus River Basin and climate change. Criterion Quarterly 10(3)

Khattak S, Ali S (2015) Assessment of temperature and rainfall trends in Punjab province of Pakistan for the period 1961-2014. J Himal Earth Sci 48:42–61

MacKerron D (2001) Climate change and global crop productivity. Edited by Reddy KR, Hodges HF. Wallingford, UK: CABI Publishing (2000), pp. 471. Experimental Agriculture, 37(2), 269–274

Maida Z, Rasul G (2010) Rise in summer hest index over Pakistan. Pakistan J Meteorol 6:12

McGrath JM, Lobell DB (2013) Regional disparities in the CO2 fertilization effect and implications for crop yields. Environ Res Lett 8(1):014054

Mendoza MET, The BD, Naret H, Ballaran VG, Arias JKB (2014) Assessing vulnerability to climate change impacts in cambodia, the philippines and vietnam: an analysis at the commune and household level. Environ Sci Manag 17(2):78–91

Metzger MJ, Schröter D (2006) Towards a spatially explicit and quantitative vulnerability assessment of environmental change in Europe. Reg Environ Change 6:201–216

Panthi J, Aryal S, Dahal P, Bhandari P, Krakauer NY, Pandey VP (2016) Livelihood vulnerability approach to assessing climate change impacts on mixed agro-livestock smallholders around the Gandaki River Basin in Nepal. Reg Environ Change 16(4):1121–1132

Rehman MA, Siddique BN, Hashmi N, Masud K, Adeel M, Rameez M, Khan A, Dawodd KM, Shah SAA, Karim M (2018) Climate change impact on rural livelihoods of small landholder: a case of Rajanpur, Pakistan. Int J Appl Agric Sci 4(2):28–34. https://doi.org/10.11648/j.ijaas.20180402.11

Richardson AG, Reddy KR, Boone ML (2002) Sensitivity analysis of climate change impacts on cotton production using the GOSSYM crop model. Int J Biotronics 31:25–49

Río S, Iqbal A, Cano-Ortiz M, Herrero A, Hassan L, Penas A (2013) Recent mean temperature trends in Pakistan and links with teleconnection patterns. Int J Climatol 33:277–290

Sadiq N, Qureshi M (2010) Climatic variability and linear trend models for the five major cities of Pakistan. J Geogr Geol 2:83–92

Saeed F, Athar H (2017) Assessment of simulated and projected climate change in Pakistan using IPCC AR4-based AOGCMs. Theor Appl Climatol 130:1–14

Saeed F, Almazroui M, Islam N, Khan MS (2017) Intensification of future heat waves in Pakistan: a study using CORDEX regional climate models ensemble. Nat Haz 87:1635–1647

Salik KM, Ishfaq S, Saeed F, Noel E, Syed Q (2015) Pakistan Country Situation Assessment. PRISE working paper

Salma S, Rehman S, Shah MA (2012) Rainfall trends in different climate zones of Pakistan. Pakistan J Meteorol  9(17):34–47

Sheikh MM, Manzoor N, Adnan M, Ashraf J, Khan AM (2009) Climate profile and past climate changes in Pakistan. Research Report No. GCISC-RR-01, Global Change Impact Studies Centre, Islamabad

Siddique R, Samad G, Nasir M, Jalil H (2012) The impact of climate change on major agricultural crops: evidence from Punjab, Pakistan. PIDE Working Paper

Singh RP, Vara PV, Sunita K, Giri SN, Reddy KR (2007) Influence of high temperature and breeding for heat tolerance in cotton: a review. Adv Agronomy 93:313–385

Ton P (2011) Cotton and climate change: impacts and options to mitigate and adapt. International Trade Centre (ITC) Geneva: ITC, 2011. xii, 32 p. (Technical paper)

Ullah S (2017) Climate change impact on agriculture of Pakistan—a leading agent to food security. Int J Environ Sci Nat Resour 6(3):555690. https://doi.org/10.19080/IJESNR.2017.06.555690

van Aalst MK, Cannon T, Burton I (2008) Community level adaptation to climate change: the potential role of participatory community risk assessment. Global Environ Change 18(1):165–179

Warszawski L, Frieler K, Huber V, Piontek F, Serdeczny O, Schewe J (2014) The inter-sectoral impact model intercomparison project (ISI–MIP): project framework. Proc Natl Acad Sci USA 111:3228–3232

Weis SWM, Agostini VN, Roth LB, Gilmer B, Schill SR, Knowles JE, Blyther R (2016) Assessing vulnerability: an integrated approach for mapping adaptive capacity, sensitivity, and exposure. Clim Change 136(3):615–629

Williams JR, Jones CA, Kiniry JR, Spanel DA (1989) The EPIC crop growth model. Trans ASAE 32:497–511

Zulfiqar MA, Sabri MA, Raza MA, Hamza A, Hayat A, Khan A (2010) Effect of Temperature and Relative Humidity on the Population Dynamics of Some Insect Pests of Maize. Pakistan J Life Soc Sci 8:16–18

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Acknowledgements

This study is based on the Pathways to Resilience in Semi-Arid Economies (PRISE) project, funded by Canada’s International Development Research Centre (IDRC) and the UK’s Department for International Development (DFID) through the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA). RISE holds the intellectual property rights to the research conducted for this paper.

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Samavia Batool & Fahad Saeed

Center for Excellence in Climate Change Research, King Abdul-Aziz University, Jeddah, Saudi Arabia

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Appendix 1: Map of the study sites

Appendix 2: District profile

2.1 dera ghazi khan.

Brief profile of the selected UCs of DGK is given here:

Kala is located between DGK canal and river Indus. It is affected by floods from rod khoi (hill torrents) as well as riverine flooding from the Indus river. We interviewed cotton farmers in two villages, namely Patti Makwal and Basti Raimen, with 60 and 400 households, respectively. Major occupation of the villagers include farming, daily wage labourer and foreign employment. Major crops planted in the villages include cotton, sugarcane, rice and wheat. Patti Makwal mostly has small farmers whereas Basti Raimen has a large number of medium-sized farmers (having more than 12.5 acres of land per household). Agricultural production in this UC is particularly affected by floods, heavy rainfall and pest attack. Although majority of the farmers produce cotton, there is no cotton farmer’s organization at the UC level. There are around 6–7 pesticide and fertilizer suppliers, located at a distance of 3 km from the village. Farmers believe that the number of providers is sufficient to cater to the demands of local farmers. However, they think that the reach of government extension department needs to be enhanced.

Mana Ahmadani is further away from the Indus river and is moderately affected by floods from hill torrents. Within this, we have covered a number of villages, namely Basti garbi, Bhabay wala, Kotla Ahmed khan, Basti noor wahi, Hala and Basti Foja, based on cotton production figures. The population in these villages ranges from 500 (50 households) to 6000 people (500 households). Major occupations include agricultural production, daily wage labour (farm workers and small factory workers) and foreign labour. Wheat, sugarcane, cotton and tobacco are the major crops of these villages. Major crop issues include rainfall variability, hailing and pest attack.

Mor Jhangi is located at the western side of the river Indus and is severely affected by floods from the Indus river. Basti Malana, being a large village and severely affected by 2010 floods, was the only village covered under this survey. It has an average of 1800 households and has 2500 acres of agricultural land. It has a good mix of small, medium and large sized farmers, but large farmers dominate the cotton production. The majority of agricultural land was destroyed during the 2010 flood. Increase in temperature and resultant outbreak of pest is another major issues facing crop production in this region.

2.2 Faisalabad

Cotton production has declined by 30.0% in Faisalabad since 1991. Cotton farmers have shifted to sugarcane production. We covered one UC in Faisalabad having a large number of cotton farmers. Farmers in UC 91 still produce a comparatively larger yield of cotton. Village Danabad was selected as a study site. There is not a single episode of flooding recorded since 1981. This site was chosen to assess other climate indicators such as temperature change and rainfall variability, which have significant impact on crop production and quality.

Appendix 3: Indicators used for the construction of vulnerability index

• a Sum of total does not add to the total number of respondents as multiple options exists for sources of information

Appendix 4: Wealth index categories as per landholdings

Following the methodology used by Vyas and kumaranayake (2016), we have constructed the wealth index, using the Principal Components Analysis (PCA). PCA helps in the identification of small uncorrelated variables in a large dataset, defines their relationship, summarizes the data and prevent loss of even minute information (Devkota et al. 2014 , Filmer and Prittchet 2001 ). PCA can be mathematically represented as follow:

where \(\bar{x}_{\text{m}}\) and \(s_{\text{m}}\) are the mean and standard deviation of asset \(x_{\text{m}}\) , and \(\varphi\) is the weight for each variable.

This division of data creates weighted components or factors that allows for interpretation of smaller components from large datasets. Table below highlights the variables used for the construction of the PCA, which includes land ownership, regular household items and access to irrigation facilities.

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Batool, S., Saeed, F. Unpacking Climate Impacts and Vulnerabilities of Cotton Farmers in Pakistan: A Case Study of Two Semi-arid Districts. Earth Syst Environ 2 , 499–514 (2018). https://doi.org/10.1007/s41748-018-0068-4

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Received : 05 April 2018

Revised : 01 August 2018

Accepted : 02 August 2018

Published : 14 August 2018

Issue Date : December 2018

DOI : https://doi.org/10.1007/s41748-018-0068-4

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