Diverse responses of winter wheat yield and water use to climate change and variability on the semiarid loess plateau in China

Crop production and water use in rainfed cropland are vulnerable to climate change. This study was to quantify diverse responses of winter wheat (Triticum aestivum L.) yield and water use to climate change on the Loess Plateau (LP) under different combinations of climatic variables. The crop model APSIM was validated against field experimental data and applied to calculate yield and water use at 18 sites on the LP during 1961 to 2010. The coefficient of variation of yield ranged from 12 to 66%, in which the vulnerability of yield increased from the southeast (12%) to the northwest (66%). This change was attributed to the gradual increase in precipitation variation from the southeast to the northwest. An obvious warming trend during 1961 to 2010 resulted in a significant decrease in the growth duration by 1 to 5 d decade-1. The yield at 12 sites was significantly reduced by 120 to 720 kg ha-1 decade-1. Evapotranspiration was significantly decreased by 1 to 26 mm decade-1; however, water use efficiency at most sites showed no significant trend. Eighteen sites were classified into three climatic zones by cluster analysis: high temperature-high precipitation-low radiation (HHL), medium temperature-medium precipitation-medium radiation (MMM), and low temperature-low precipitation-high radiation (LLH). The trend of decreasing yield was smallest in the HHL cluster because of a minimal reduction in precipitation, while decreasing trends in yield and evapotranspiration were larger in the LLH and MMM because of larger reductions in precipitation. The results imply that among strategies such as breeding for long duration or drought tolerance, modification of the planting date will be necessary to avoid high temperatures associated with climate change. © 2014 by the American Society of Agronomy

The Feasibility of Growing Switchgrass in China for Lignocellulosic Ethanol Production

Switchgrass (Panicum virgatum L.) is a perennial plant species native to the United States that is capable of adapting to a wide variety of geographic and climate conditions. There are two ecotypes of switchgrass: lowland varieties which favor areas with higher rainfall and longer growing seasons and upland varieties which favor areas with cooler and drier climate conditions with shorter growing seasons. Switchgrass has the capacity to become a significant bioenergy feedstock for lignocellulosic ethanol conversion. The purpose of this dissertation is to determine which regions in China are suitable for switchgrass production, estimate potential biomass yield, and examine the effects of predicted climate change scenarios at the end of the 21st century on potential yields in China. To accomplish these goals, two ecological niche models (Maxent and GARP) are implemented based on known switchgrass presence data throughout the United States to ascertain which regions in China have suitable habitats for its growth. Multiple linear regression analysis was performed on a comprehensive database of 1,190 switchgrass field trials in 39 separate locations across the United States to build a model that estimates potential switchgrass yields across China. Future climate projections (2070 – 2099) from the Hadley Centre Coupled Model, version 3 (HadCM3) global circulation model (GCM) are employed in the multiple linear regression model to make switchgrass yield estimations for the end of the century. The ecological niche modeling results reveal China has large areas of suitable habitat for switchgrass development. The multiple linear regression analysis demonstrates that China has the potential to produce large quantities of switchgrass, even more so than in the United States; however, analysis of the impact of climate change by the end of the 21st Century indicates that warmer temperatures will result in lower yields on average, a substantial reduction in suitable habitat for lowlands, and an expanded habitat range for upland ecotypes. This dissertation concludes that switchgrass should be considered a viable plant species to serve as a bioenergy feedstock for lignocellulosic ethanol production in China, and the results herein offer guidelines regarding optimal regions in the country for switchgrass production

The Feasibility of Growing Switchgrass in China for Lignocellulosic Ethanol Production

Switchgrass (Panicum virgatum L.) is a perennial plant species native to the United States that is capable of adapting to a wide variety of geographic and climate conditions. There are two ecotypes of switchgrass: lowland varieties which favor areas with higher rainfall and longer growing seasons and upland varieties which favor areas with cooler and drier climate conditions with shorter growing seasons. Switchgrass has the capacity to become a significant bioenergy feedstock for lignocellulosic ethanol conversion. The purpose of this dissertation is to determine which regions in China are suitable for switchgrass production, estimate potential biomass yield, and examine the effects of predicted climate change scenarios at the end of the 21st century on potential yields in China. To accomplish these goals, two ecological niche models (Maxent and GARP) are implemented based on known switchgrass presence data throughout the United States to ascertain which regions in China have suitable habitats for its growth. Multiple linear regression analysis was performed on a comprehensive database of 1,190 switchgrass field trials in 39 separate locations across the United States to build a model that estimates potential switchgrass yields across China. Future climate projections (2070 – 2099) from the Hadley Centre Coupled Model, version 3 (HadCM3) global circulation model (GCM) are employed in the multiple linear regression model to make switchgrass yield estimations for the end of the century. The ecological niche modeling results reveal China has large areas of suitable habitat for switchgrass development. The multiple linear regression analysis demonstrates that China has the potential to produce large quantities of switchgrass, even more so than in the United States; however, analysis of the impact of climate change by the end of the 21st Century indicates that warmer temperatures will result in lower yields on average, a substantial reduction in suitable habitat for lowlands, and an expanded habitat range for upland ecotypes. This dissertation concludes that switchgrass should be considered a viable plant species to serve as a bioenergy feedstock for lignocellulosic ethanol production in China, and the results herein offer guidelines regarding optimal regions in the country for switchgrass production

Breaking the spiral of unsustainability : an exploratory land use study for Ansai, the Loess Plateau of China

Serious soil loss, food insecurity, population pressure, and low income of the rural population are interrelated, and consequently result in a spiral of unsustainability in the Loess Plateau, China. This thesis takes Ansai County in the Loess Plateau of China as a case study, to explore strategic land use options that may break the unsustainability spiral and meet goals of regional development. A systems analysis approach has been applied, in which fragmented and empirical information of the biophysical and agronomic conditions is integrated with well-adapted production ecological principles and other knowledge sources.With respect to the land use problems and regional development objectives, alternative production activities (systems) have been identified and quantified using a 'target-oriented approach' and the concept of 'best technical means', and based on information obtained from a quantitative land evaluation (based on the EPIC model), experimental data, literature and expert knowledge. Production activities have been quantified for cropping, fruit, grassland and firewood production systems, and animal husbandry. Production techniques emphasize soil conservation, productivity, use efficiency or low emission of chemicals. The quantified production activities, resource constraints, and socio-economic and environmental objectives have been incorporated into a multiple goal linear programming model that is used to optimize land use allocation, evaluate trade-offs among objectives and evaluate policy scenarios.The results reveal that the goals of food security and soil conservation in Ansai can be easily achieved from a biophysical and agro-technical point of view. Current slope cultivation and the resulting serious soil loss can be greatly reduced, while still guaranteeing food security for the rural population (in 2020). The soil loss control is, to a large extent, in line with the goals of increasing crop productivity and labor productivity (net agricultural return per laborer). In the long term, terracing and crop rotations with alfalfa could be the best options for soil conservation and also for agricultural production. The large rural labor force can be used for terrace construction. Alfalfa can fix nitrogen, and thus greatly reduce the demand for fertilizer N, and also improve soil fertility.The large rural population and the lack of off-farm employment opportunities could be the most important factor affecting rural development in Ansai. This is evident from the trade-off results, i.e., increasing the total employment in agriculture leads to an apparent adverse effect on many other objectives. However, there is a potential for maintaining high agricultural employment at a reasonable income level. The current low net return due to the very limited external inputs and poor crop and soil management can be substantially improved by efficient resource use and appropriate inputs.This research work contributes to the understanding of regional problems and agricultural development potentials. The results show agro-technical possibilities for breaking the spiral of unsustainability in this very fragile and poorly endowed region. Soil conservation, food security, employment and income for the rural population can be greatly enhanced by appropriate land use and agro-techniques. To promote actual development towards the identified options, appropriate policy measures aimed at improving the land tenure system and controlling population growth must be developed and implemented. The explored land use options enable a much more targeted policy development. In addition, the study can contribute to the formulation of a research agenda for research at field, crop and animal level.</p

Soil Water Erosion

The purpose of this book is to provide novel results related to soil water erosion that could help landowners and land-users, farmers, politicians, and other representatives of our global society to protect and, if possible, improve the quality and quantity of our precious soil resources. Published papers on the topics are related to new ways of mapping, maps with more detailed input data, maps about areas that have never been mapped before, sediment yield estimations, modelling sheets and gully erosion, USLE models, RUSLE models, dams which stop sediment runoff, sediment influx, solute transport, soil detachment capacities, badland morphology, freeze-thaw cycles, armed conflicts, use of rainfall simulators, rainfall erosivity, soil erodibility, etc

Water stress in maize production in the drylands of the Loess Plateau

Water stress during two maize (Zea mays L.) growing seasons (2013 and 2014) was investigated in a semiarid region of northwest China. The HYDRUS-1D model gave good simulation of the soil water content in different layers throughout a 0- to 200-cm depth during the maize growing season, with R-2 values of 70.6 and 77.0% for the calibration and validation periods, respectively. Water stress for maize production was observed in June of 2013 and in July of 2014. The soil water storage (SWS) decreased significantly during the early stage of the maize growing season, especially in 2014. The root depth and crop height were 20 cm deeper and 100 cm higher, respectively, in 2014 than in 2013 at the early stage. These results suggest that in the early stage of the maize growing season, pre-seeding SWS can alleviate crop water stress effectively via deep roots. Model simulation showed that the plow pan layer (at a depth of 20-40 cm), with high soil bulk density and a lower soil water retention curve, significantly reduced infiltration. High evapotranspiration and low precipitation result in a temporary dry layer during the early stage, highlighting the plow pan as the sensitive layer for water stress during the drought period. Effective management practices such as deep plowing, plastic film mulching, or water conservation treatments in the fallow period are needed to avoid the formation of this temporary dry layer during the drought period at the early stage and thus improve maize production in rainfed agriculture on the Loess Plateau of China

Crop management options to reduce nitrogen pollution in Liangzihu lake basin, Central China

In Central China, high mineral nitrogen (N) application rates lead to low N recovery and high N losses. Large amounts of the nitrate-N leached from agricultural soil end up in aquatic ecosystems, which negatively affects both ecosystem and human health. Such effects are particularly pronounced in the Liangzihu Lake basin, Central China, where application of mineral N to the predominating maize-wheat rotation systems on coarse-textured soils can exceed 300 kg ha-1. We hypothesize that improved crop management can reduce the current nitrate-N pollution while enhancing system performance. The present study initially identified the main drivers of excessive N use by household surveys. Subsequent field experiments between 2012 and 2013 evaluated the effects of modified fertilizer-N management and the use of N-catching cover crops on soil-N dynamics, N-use efficiency, yield of maize and wheat, and nitrate-N leaching. Finally, the field trial data were used to parameterize the Environmental Policy Integrated Climate (EPIC) model to estimate N leaching losses under current and alternative crop and N management. Current N application rates average 229 kg N ha-1 season-1, which is higher than the cereal crop requirements of 150-180 kg N ha-1. The main reasons for the excessive use of mineral N are related to low farmland productivity (r = -0.184, p = 0.003), small farm size (r = -0.168, p = 0.006), a high share of off-farm income (coefficient = 25.94, p = 0.003), and a low education level of the household head (coefficient = -11.20, p = 0.034). The field experiment could show that cultivating a cover crop combined with a reduced application rate (290 kg N ha-1 in 3 splits) and multiple splitting of mineral N fertilizer can achieve similar yields (6.4-6.9 Mg ha-1) to those obtained with current management (470 kg N ha-1 in 2 splits). In addition, this alternative crop and fertilizer management increased the agronomic N-use efficiency by 7 kg grain kg-1 N applied in both wheat and maize, and enhanced the N-fertilizer recovery by 15% in wheat and 20% in maize. In addition, nitrate-N leaching was reduced by 15 kg N ha-1 in both the first-year maize and wheat crops. Once calibrated with the data from the field experiment, the EPIC model was able to predict crop biomass and the soil water content under moderate (long-term mean) climate conditions with a determination coefficient higher than 0.5 and a model bias of less than 3%. However, the model underestimated the soil water content in the drought seasons with a bias of >36%. Moreover, it tended to slightly overestimate nitrate-N leaching with 13-181 kg N ha-1 for the entire experimental period and in both 1 m and 1.8 m soil depths. It is concluded that (1) the current N application rate in the study area is excessive because of insufficient awareness and the easy and low-cost availability of mineral-N fertilizers, (2) the currently high N losses from crop fields can be substantially reduced by reducing application rates and by replacing bare fallow periods with legume cover crops without negative trade-offs in crop yields, and (3) the calibrated EPIC model can be used to predict the aboveground crop biomass and the soil water content, but it tends to overestimate nitrate-N leaching. Consequently, there is a need to inform farmers about the negative effects of excessive N use, popularize alternative agronomic management options, and adapt the existing EPIC model to improve the prediction of nitrate pollution in Central China

Impacts of climate change and increasing carbon dioxide levels on yield changes of major crops in suitable planting areas in China by the 2050s

Maize, rice, and wheat are the major staple food crops in China and are crucial components of national food security and economic development. The cultivation and production of these crops are expected to be affected by climate change and elevated atmospheric carbon dioxide (CO2) concentration, and have drawn considerable public attention. The objective of this experiment was to understand the impact of future climate change (including increased temperature and changed precipitation patterns) and elevated CO2 concentration on variations of crop yields in their suitable planting areas. We conducted a spatial grid-based analysis of maize, rice, and wheat yields using projections of future climate generated by a multi-model ensemble of global climate models for three representative concentration pathway scenarios (RCP2.6, RCP4.5, and RCP8.5) in suitable planting areas in China for the 2030s (2021–2040) and the 2050s (2041–2060). Suitable areas for the planting of maize, rice, and wheat under the high-emission scenarios migrated slightly northward over time. Yield of all three crops would be expected to remain stable or to slightly increase across China in the future. A possible reason for this result may be because the positive effects of increased precipitation and CO2 offset the negative effect of increased temperature on crop yields, resulting in a much more appropriate growth environment and increased biomass accumulation and crop yield. In addition, this study also indicated that changes in crop yields were mainly driven by temperature and CO2 factors. The potential effects of climate change and elevated CO2 concentration on migration of planting areas and yield fluctuations for crops should be given greater attention in the future

Estimating NH3 emissions from agricultural fertilizer application in China using the bi-directional CMAQ model coupled to an agro-ecosystem model

Atmospheric ammonia (NH3) plays an important role in atmospheric aerosol chemistry. China is one of the largest NH3 emitting countries with the majority of NH3 emissions coming from agricultural practices, such as fertilizer application and livestock production. The current NH3 emission estimates in China are mainly based on pre-defined emission factors that lack temporal or spatial details, which are needed to accurately predict NH3 emissions. This study provides the first online estimate of NH3 emissions from agricultural fertilizer application in China, using an agricultural fertilizer modeling system which couples a regional air quality model (the Community Multi-scale Air Quality model, or CMAQ) and an agro-ecosystem model (the Environmental Policy Integrated Climate model, or EPIC). This method improves the spatial and temporal resolution of NH3 emissions from this sector. We combined the cropland area data of 14 crops from 2710 counties with the Moderate Resolution Imaging Spectroradiometer (MODIS) land use data to determine the crop distribution. The fertilizer application rates and methods for different crops were collected at provincial or agricultural region levels. The EPIC outputs of daily fertilizer application and soil characteristics were input into the CMAQ model and the hourly NH3 emissions were calculated online with CMAQ running. The estimated agricultural fertilizer NH3 emissions in this study were approximately 3 Tg in 2011. The regions with the highest modeled emission rates are located in the North China Plain. Seasonally, peak ammonia emissions occur from April to July. Compared with previous researches, this study considers an increased number of influencing factors, such as meteorological fields, soil and fertilizer application, and provides improved NH3 emissions with higher spatial and temporal resolution