Irrigation is a land management practice with major environmental impacts. However, global energy consumption and carbon emissions resulting from irrigation remain unknown. We assess the worldwide energy consumption and carbon emissions associated with irrigation, while also measuring the potential energy and carbon reductions achievable through the adoption of efficient and low-carbon irrigation practices. Currently, irrigation contributes 216 million metric tons of CO₂ emissions and consumes 1896 petajoules of energy annually, representing 15% of greenhouse gas emissions and energy utilized in agricultural operations. Despite only 40% of irrigated agriculture relies on groundwater sources, groundwater pumping accounts for 89% of the total energy consumption in irrigation. Projections indicate that future expansion of irrigation could lead to a 28% increase in energy usage. Embracing highly efficient, low-carbon irrigation methods has the potential to cut energy consumption in half and reduce CO₂ emissions by 90%. However, considering country-specific feasibility of mitigation options, global CO₂ emissions may only see a 55% reduction. Our research offers comprehensive insights into the energy consumption and carbon emissions associated with irrigation, contributing valuable information that can guide assessments of the viability of irrigation in enhancing adaptive capacity within the agricultural sector.

Multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA) and component analysis/factor analysis (PCA/FA), were applied to explore the surface water quality datasets including 14 parameters at 28 sites of the Eastern Poyang Lake Basin, Jiangxi Province of China, from January 2012 to April 2015, characterize spatiotemporal variation in pollution and identify potential pollution sources. The 28 sampling stations were divided into two periods (wet season and dry season) and two regions (low pollution and high pollution), respectively, using hierarchical CA method. Four parameters (temperature, pH, ammonia-nitrogen (NH₄-N), and total nitrogen (TN)) were identified using DA to distinguish temporal groups with close to 97.86% correct assignations. Again using DA, five parameters (pH, chemical oxygen demand (COD), TN, Fluoride (F), and Sulphide (S)) led to 93.75% correct assignations for distinguishing spatial groups. Five potential pollution sources including nutrients pollution, oxygen consuming organic pollution, fluorine chemical pollution, heavy metals pollution and natural pollution, were identified using PCA/FA techniques for both the low pollution region and the high pollution region. Heavy metals (Cuprum (Cu), chromium (Cr) and Zinc (Zn)), fluoride and sulfide are of particular concern in the study region because of many open-pit copper mines such as Dexing Copper Mine. Results obtained from this study offer a reasonable classification scheme for low-cost monitoring networks. The results also inform understanding of spatio-temporal variation in water quality as these topics relate to water resources management.