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Background: Chickpea is one of the major legume crops being cultivated in the arid and semi-arid regions of Pakistan. It is mainly grown on the marginal areas where, terminal drought stress is one of the serious threats to its productivity. For defining the appropriate selection criteria for screening drought tolerant chickpea genotypes, present study was conducted. Distinct chickpea germplasm was collected from different pulses breeding institutes of Pakistan and evaluated for drought tolerance at germination and early seedling stages, furthermore, at late vegetative growth stages physiochemical traits and multi-environment yield performance were also tested. Results: Chickpea genotypes under different environments, were significantly varied for different seedling traits, physio-chemical attributes and seed yield. Genotypes showing drought tolerance by performing better at an early seedling stages were not correspondingly high yielding. Screening for drought tolerance on seed yield basis is the most appropriate trait to develop the drought tolerant as well as high yielding chickpea genotypes. Results confirmed that traits of early growth stages were not reflecting the drought tolerance at terminal growth stages and also did not confer high yielding. NIAB-rain fed environment proved ideal in nature to screen the chickpea genotypes whereas, NIAB-lysimeter and Kalur Kot was least effective for selecting genotypes with high seed yield. Genotypes D0091

A single pre-harvest spray of MJ (10 mmol L(-1)) at 169 DAFB or MJ (5 mmol L(-1)) at 186 DAFB was effective in improving the red blush and export grade fruit through accumulation of flavonoids in fruit skin without adversely affecting quality at harvest.

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This paper attempts to identify sources of resource use inefficiency for cotton production in Pakistan's Punjab. The use of a non-parametric method, Data Envelopment Analysis (DEA), is developed to study the relative technical and allocative efficiencies of individual farms which use similar inputs, produce the same product and operate under comparable circumstances. In the 'cotton-wheat' system of Pakistan, there are a considerable number of farms that are both technically and allocatively inefficient. The use of DEA shows that the technique provides a clear identification of both the extent and the sources of technical and allocative inefficiencies in cotton production. However, both the interpretation of the farm level results generated and the projection of these results to a higher level require care, given the technical nature of the agricultural production processes.

This paper attempts to identify sources of resource use inefficiency for cotton production in Pakistan's Punjab. The use of a non-parametric method, Data Envelopment Analysis (DEA), is developed to study the relative technical and allocative efficiencies of individual farms which use similar inputs, produce the same product and operate under comparable circumstances. In the 'cotton-wheat' system of Pakistan, there are a considerable number of farms that are both technically and allocatively inefficient. The use of DEA shows that the technique provides a clear identification of both the extent and the sources of technical and allocative inefficiencies in cotton production. However, both the interpretation of the farm level results generated and the projection of these results to a higher level require care, given the technical nature of the agricultural production processes.

The world has been waging a fight against the novel coronavirus (COVID-19) since December 2019. The current coronavirus crisis is a catastrophe affecting billions of families worldwide. So far, COVID-19 has wreaked havoc across the globe: by slowing down economic growth; decreasing global trade; hurting health sector; increasing unemployment and underemployment; reducing FDI and hurting the tourism sector. This study investigates the economic costs of COVID-19. By using descriptive analysis, this study shows that the major economic variables, such as economic growth, global trade, health sector, unemployment and underemployment, foreign direct investment and travel and tourism sector have significantly affected by COVID-19.