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The primary aspiration of this paper is to learn about the effects of economic growth, energy consumption, agriculture and irrigation water consumption and agriculture productivity on environmental pollution in five countries of Central Asia. The data cover the period from 1992 to 2020 by applying panel data models, namely the Panel FMOLS, Panel DOLS and Panel ARDL-PMG approaches. The results indicate that there is a positive long-term impact of economic growth, water productivity, energy consumption and electricity production on CO2 emissions while agriculture value added and trade openness have a negative and statistically significant influence on CO2 emissions in Central Asia. Country specific short-run coefficients from Panel ARDL reveal that energy consumption is the main driver for rise in the level of CO2 emissions in the countries under the study. Indeed, country level analysis generates unique nexus correlation among agriculture, energy and environmental degradation in each country of Central Asia. [ABSTRACT FROM AUTHOR]

The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing "more crop per drop" (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involving crop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m3/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m3) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fraction by reference ET. The ET fraction was determined using Landsat thermal imagery by selecting the "hot" pixels (zero ET) and "cold" pixels (maximum ET). The grass reference ET was calculated by FAO Penman- Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices. [ABSTRACT FROM AUTHOR]