Farmers’ Participatory Alternate Wetting and Drying Irrigation Method Reduces Greenhouse Gas Emission and Improves Water Productivity and Paddy Yield in Bangladesh

A global meta-analysis of yield-scaled N2 O emissions and its mitigation efforts for maize, wheat, and rice

Maintaining or even increasing crop yields while reducing nitrous oxide (N2 O) emissions is necessary to reconcile food security and climate change, while the metric of yield-scaled N2 O emission (i.e., N2 O emissions per unit of crop yield) is at present poorly understood. Here we conducted a global meta-analysis with more than 6000 observations to explore the variation patterns and controlling factors of yield-scaled N2 O emissions for maize, wheat and rice and associated potential mitigation options. Our results showed that the average yield-scaled N2 O emissions across all available data followed the order wheat (322 g N Mg-1 , with the 95% confidence interval [CI]: 301-346) > maize (211 g N Mg-1 , CI: 198-225) > rice (153 g N Mg-1 , CI: 144-163). Yield-scaled N2 O emissions for individual crops were generally higher in tropical or subtropical zones than in temperate zones, and also showed a trend towards lower intensities from low to high latitudes. This global variation was better explained by climatic and edaphic factors than by N fertilizer management, while their combined effect predicted more than 70% of the variance. Furthermore, our analysis showed a significant decrease in yield-scaled N2 O emissions with increasing N use efficiency or in N2 O emissions for production systems with cereal yields >10 Mg ha-1 (maize), 6.6 Mg ha-1 (wheat) or 6.8 Mg ha-1 (rice), respectively. This highlights that N use efficiency indicators can be used as valuable proxies for reconciling trade-offs between crop production and N2 O mitigation. For all three major staple crops, reducing N fertilization by up to 30%, optimizing the timing and placement of fertilizer application or using enhanced-efficiency N fertilizers significantly reduced yield-scaled N2 O emissions at similar or even higher cereal yields. Our data-driven assessment provides some key guidance for developing effective and targeted mitigation and adaptation strategies for the sustainable intensification of cereal production.

Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin

To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temporal distribution characteristics and driving factors of water productivity of the main crops in the Yellow River Basin were analyzed with the spatial autocorrelation analysis, grey correlation analysis, sensitivity analysis, and relative contribution rate. The results showed that the minimum mean values of WPI, WPC, and WPT were 0.22, 0.35, and 0.18 kg/m³ in Qinghai, respectively, the maximum mean value of WPI was 2.11 kg/m³ in Henan, and the maximum mean values of WPC and WPT were 0.71 and 0.61 kg/m³ in Shandong, respectively. The changing trends in WPI and WPT in Qinghai and in WPC in Shandong were insignificant, whereas the WPI, WPC, and WPT in other provinces showed a significant increasing trend. Water productivity displayed a certain spatial clustering feature in the Yellow River Basin in different years, such as a high-high (H-H) aggregation in Henan in 2005, and an H-H aggregation in Shanxi in 2015 for WPI. The water productivity had a significant positive correlation with the consumption of chemical fertilizer with a 100% effective component (CFCEC), effective irrigated area (EIA), plastic film used for agriculture (PFUA), and total power of agricultural machinery (AMTP), while it had a significant negative correlation with the persons engaged in rural areas (PERA). There was a large grey correlation degree between the water productivity and the average annual precipitation (AAP), CFCEC, PFUA, consumption of chemical pesticides (CFC), and AMTP in the Yellow River Basin, but their sensitivity was relatively small. The main driving factors were EIA (8.98%), agricultural water (AW, 15.55%), AMTP (12.64%), CFCEC (12.06%), and CPC (9.77%) for WPI; AMTP (16.46%), CFCEC (13.25%), average annual evaporation (AAE, 12.94%), EIA (10.49%), and PERA (10.19%) for WPC; and EIA (14.26%), AMTP (13.38%), AAP (12.30%), CFCEC (10.49%), and PFUA (9.69%) for WPT in the Yellow River Basin. The results can provide support for improving the utilization efficiency of agricultural water resources, optimizing the allocation of water resources, and implementing high-quality agricultural developments in the Yellow River Basin.