The effects of Agriculture Productivity, Land Intensification, on Sustainable Economic Growth: A panel analysis from Bangladesh, India, and Pakistan Economies.

Population in South Asia is increasing ever than a faster rate, subsequently; food security, climate change, and capital intensive agro farming techniques are the prevailing challenges in this region. This is a tri-country penal analysis, Pakistan, India, and Bangladesh, and the study covers the data throughout (1973-2020). This study has used modern farm input data besides demographic variables in the study. In this study, we use panel data set, ARDL (PMG) approach, autoregressive distributed lag model pooled mean group, which is an extensively dynamic modeling technique for heterogeneous data. The results of the study explore that transition in the demographic pattern in Pakistan, India, and Bangladesh is the real cause of low crop productivity and land intensification. Technology innovation is the only ray of hope to fulfill the food demand of the future ahead and climate agriculture practices can hamper the further deterioration of the small farmlands.

The relationship between staple food crops consumption and its impact on total factor productivity: does green economy matter?

The agriculture sector is a key driver of economic growth and provides employment opportunities across the globe generally. However, in today's world, agricultural product demand is more influenced by taste, prices, and nutritional value due to climatic variation. The study has analyzed the current situation grain productivity by using the data of farm inputs and major grain crops of Pakistan from (1960-2020). The study consists of a two-stage analysis in the first stage, the total factor productivity (TFP) variable is obtained by using the parametric Tornqvisit-Theil index output-input-aggregation method separately for each crop; rice, maize, and wheat. After that, the unit root test is used to check the stationarity and trend of the variables in the long run. Subsequently, the autoregressive distributed lag (ARDL) model is applied to check the existence of cointegration in the long run and short run among the variables. The results of the study disclosed that the consumption of rice has a positive relationship with its total factor productivity, but, wheat and maize have a negative long-run cointegration relationship with the respective productivities. The study results have shown that the consumption pattern of staple crops has substantially changed, due to climatic variation, and the current food consumption trend is revealing new dimensions and trends owing to variation in climate change and anthropogenic pressure which demands to adapt climate resilient farm practices.