Achieving sustainability of the cereal system in the Indo-Gangetic Plains（IGP）of India under progressive climate change and variability necessitates adoption of practices and technologies that increase food producti...Achieving sustainability of the cereal system in the Indo-Gangetic Plains（IGP）of India under progressive climate change and variability necessitates adoption of practices and technologies that increase food production,adaptation and mitigation in a sustainable way.This paper examines conservation agriculture（CA）from the perspective of：（i）increased yield and farm income,（ii）adaptation to heat and water stresses,and（iii）reduction in greenhouse gas（GHGs）emissions.The analyses and conclusions are based on the literature and evidences from a large number of on-station as well as farmers’field trials on CA in the cereal systems of IGP.Our analyses show that CA-based system substantially reduces the production cost（up to 23%）but produces equal or even higher than conventional system;thereby increasing economic profitability of production system.CA-based production systems also moderated the effect of high temperature（reduced canopy temperature by 1–4°C）and increased irrigation water productivity by 66–100%compared to traditional production systems thus well adapting to water and heat stress situations of IGP.Our continuous monitoring of soil flux of CO2,N2O and CH4 revealed that CA-based rice-wheat systems emit 10–15%less GHGs than conventional systems.This is the first time that CA and its components are synthesized and analyzed from food security-climate change nexus.From this holistic analysis,we suggest that wide-scale promotion of suitable CA practices by integrating into national agriculture development strategy is a way forward to address food security,climate change adaptation and mitigation challenges faced by present agriculture.展开更多
In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and ...In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 <span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted </span><span style="font-family:Verdana;">seasonal rainfall for the better performing models</span></span><span style="font-family:Verdana;">—GFDL-CM2p5-FLOR-A06,</span><span style="font-family:Verdana;"> CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012</span><span style="font-family:Verdana;">—</span><span style="font-family:Verdana;">is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.</span>展开更多
基金Financial supports for these works come from Bayer CropScienceIndian Council of Agricultural Research(ICAR)CGIAR’s research program(CRP)on Climate Change Agriculture and Food Security(CCAFS)
文摘Achieving sustainability of the cereal system in the Indo-Gangetic Plains（IGP）of India under progressive climate change and variability necessitates adoption of practices and technologies that increase food production,adaptation and mitigation in a sustainable way.This paper examines conservation agriculture（CA）from the perspective of：（i）increased yield and farm income,（ii）adaptation to heat and water stresses,and（iii）reduction in greenhouse gas（GHGs）emissions.The analyses and conclusions are based on the literature and evidences from a large number of on-station as well as farmers’field trials on CA in the cereal systems of IGP.Our analyses show that CA-based system substantially reduces the production cost（up to 23%）but produces equal or even higher than conventional system;thereby increasing economic profitability of production system.CA-based production systems also moderated the effect of high temperature（reduced canopy temperature by 1–4°C）and increased irrigation water productivity by 66–100%compared to traditional production systems thus well adapting to water and heat stress situations of IGP.Our continuous monitoring of soil flux of CO2,N2O and CH4 revealed that CA-based rice-wheat systems emit 10–15%less GHGs than conventional systems.This is the first time that CA and its components are synthesized and analyzed from food security-climate change nexus.From this holistic analysis,we suggest that wide-scale promotion of suitable CA practices by integrating into national agriculture development strategy is a way forward to address food security,climate change adaptation and mitigation challenges faced by present agriculture.
文摘In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 <span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted </span><span style="font-family:Verdana;">seasonal rainfall for the better performing models</span></span><span style="font-family:Verdana;">—GFDL-CM2p5-FLOR-A06,</span><span style="font-family:Verdana;"> CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012</span><span style="font-family:Verdana;">—</span><span style="font-family:Verdana;">is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.</span>
