These HYV seeds were supplemented by irrigation facilities and mechanization, during green revolution consequently, productivity accelerated vigorously in late in 1960s. At very first, Wizarat (
1981) calculates total factor productivity of Pakistan agriculture sector by employing arithmetic input–output index approach. The objective of the study is to find the sources of agriculture growth in Pakistan. The data comprise over the period from 1953 to 1978, pre- and post-green revolution. Results of the study disclose that live stalk and private tub wells accelerated the growth of the sector during the year 1959–1960, but the real revolution in the sector came after 1967–1968 when high yield variety seeds were introduced by the government. In nutshell, Wizarat’s (
1981) study provides the base for further research on the topic of technological advancement impact on the agriculture sector. After her, Eisner (
1985) said that it is the variation in life and lasting of inputs which are used in the production function. In economics, it is recognized that investment should include improvement in the land, development of human, and social capital that is called human capital formation. Human capital is the stalk of knowledge, expertise, and management characteristics, (Coen and Eisner
1987). Moreover, total factor productivity is the measure, which estimates and takes into account all the factor and inputs, because it is free and independent of partial factor productivities shortcomings. Rosegrant and Evenson (
1993) identified that modern farm inputs to increase the productivity of the crop. The study measures the total productivity of the South Asian region using the data from 1960 to 1985. The study is a comparative analysis of partial factor productivities, for rice, and wheat between Pakistan India and Bangladesh. Results of study discloses that yield kg per hectare in Pakistan in terms of wheat has increased substantially, since the introduction of high yield variety seeds. It is true that resources are scarce so as innovation provides ease to farmers they tended to invest more, the notable point is that research and government expenditure on to the extension of the infrastructure of rural areas, and education integrates the growth more rapidly. Dean et al. (
1998), and Fernald and Ramnath (
2004) raised the importance of many other inputs, and they called total factor productivity a multifactor method to estimate the productivity and growth of the agriculture sector. Khan (
1997) estimated Pakistan’s agriculture TFPc for the overall agriculture sector and uses data set from 1960 to 1996. He found that since technology has commenced output has increased substantially. Sabir and Ahmad’s (
2003) study explores that economic growth in Pakistan’s agriculture sector was higher during and soon after the reform period due to thence government being committed. Chang and Zepeda (
2001) indicates that although there are three sources of productivity, land–labor, and capital, and the fourth one is “public investment on human capital, because it plays critical role. In her theoretical study, she emphasized that examination of extension in knowledge is a new element to get true results of agriculture productivity, particularly in the least developed countries. The previous study literature review explores that most of the economist used Tornqvist–Theil Index approach and ordinary least square as a method of estimation; besides Tornqvist–Theil Index, some of the economists applied Malmquist Index approach. Moreover, some of the agriculture economist of India like Chaudhary (
2012) estimates the total factor productivity of Indian agriculture state wise. For this study, she employs non-parametric sequential Malmquist,
1 total productivity index using data from 1983 to 2006. In the study, total factor productivity is further decomposed in technical and efficiency changes across the states. Results of the study reveal that there are very few states in India where technical change is the main reason for improvement. However, contrast to technical change, many of the sample states of the country are not much efficient to reap the benefit of technological change, thereby farmers of the states are not producing the optimum level of agriculture output. Rehman et al. (
2016) investigate the relationship between agriculture gross domestic product (AGDP) and cotton yield kg per hectare. The study further included fertilizer consumption, the area under the crop over the period from 1970 to 2015. He uses ADF, to find stationarity of the variables in the long run, then uses cointegration test, for regression analysis, and finally, he employs ordinary least square, (OLS) method. Results of the study explore that output of cotton, fertilizer consumption, has a positive relationship with the agriculture GDP, whereas area has a negative relationship. In previous studies of agricultural growth and technological advancement impact on the sector, capital and labor had been the major inputs. Literature review for this study explores that total factor productivity is the best measure to capture the effects of technological advancement on a wide range of crops and overall agriculture sector, because total factor productivity (TFPC) is basically a residual of output and input. Therefore, the difference of variation in output due to variation of input application gives insight knowledge about the impact of modern farm inputs usage on crop growth per ha
Since the 1960s, intensive population growth has exacerbated land utilization; thereby, further extension in the cultivated area has become very limited, due to being used of agricultural land for residential purpose. Thereby, because of this problem, yield kg per hectare of the crop and overall agriculture output can only be increased by increasing factor productivity of the sector. It was a new classical economist Solow (
1957) and Kendrick (
1973) who presented the theory of innovation regarding technological advancement. The theory takes into account a robust increase in population on given natural resources, land, labor, and water especially. Prior to the green revolution, economist used partial factor productivity index to measure productivity of the sector, but it gave misleading results, consequently, Denison (
1967), Kendrick (
1973), and Christensen (
1975) attach the importance to total factor productivity index, and finally, Solow (
1957) devised it. The index is a weighted sum of all input and output indices, the input–output included land, labor, capital stalk, chemical fertilizer conventional agricultural inputs, and the index generally includes land, labor, and physical and human capital. Many of the studies have been conducted in Pakistan, India, and other regions of the world in which total factor productivity index has been used to get the true level of productivity of the agriculture sector by employing various approaches like Wizarat (
1981), used arithmetic index approach to get TFPc results using data from 1953 to 1979, rose Rosegrant and Evenson (
1993), Saleem et al. (
2019), Sarel and Robinson (
1997) Cornejo and Shumway (
1997), Shabbir (
2015,
2016), Antle and Capalbo (
1988), Jin et al. (
2002), Ali and Byerlee (
2000), Coelli and Rao (
2003), Mukherjee and Kuroda (
2003), Ali (
2004), and used Tornqvist–Theil Index approach, to get TFPC results. However, Wizarat (
1981)’s study has many shortcomings such as non-availability of data, and perfect substitutability between inputs which make her analysis incomplete. Rosegrant and Evenson (
1993) investigated the gap between the productivity of India and Pakistan using the data from 1956 to 1985 for rice and wheat productivity in Indian and Pakistan’s Punjab. In most of the studies, total factor productivity index computed aggregate agriculture output of all crops including major and minor like wheat, maize, sugarcane, vegetables, and pulses, whereas in inputs, land labor, capital (tractors, tube wells, and animals) besides fertilizers and pesticides quantity with respect to their prices.
There are some country-specific studies about some other problems of cotton such as Shuli et al. (
2018) overview cotton and its future prospectus, and Sadashivappa and Qaim (
2009) analyzes BT cotton development and role of government in seed price intervention, Ashraf et al. (
2018) discuss about the future of cotton and its outlook on the year of 2025 using data from 1990 to 2018. In this study, compound growth rate formula has been used to get the results for forecasting years ahead. Results of the study show an increase in the area of cotton under the cultivation, but in reality, we are observing that instead of increasing area of the crop is decreasing. However, none of the studies is available on to date, whichever estimates total factor productivity of cotton particularly in terms of comparative advantage with respect to technological advancement impact on India and Pakistan.