Fulu TaoLUKE, CAS
Skills and Expertise
EnvironmentSustainabilityWater QualityEnvironmental Impact AssessmentAgricultureClimate ChangeWater Resources ManagementCrop ProductionStatistical ModelingSoilRiversHydrological ModelingSoil and Water ConservationClimate Change and AgricultureHydrologyClimate VariabilityWater BalanceIrrigationFood SecurityMeteorologyPrecipitationWater ResourcesClimateSoil ErosionEvapotranspirationAquacultureCarbon DioxideMonitoringDroughtCalibrationWeatherCropWheatEvaporationRiceCrop ModelingMaizeClimate Change ImpactsCropping SystemsAgrometeorologyGlobal Climate ModelHeat StressSPICold Stress
Research Item (219)
- Aug 2018
Please feel free to use the link below to access the final version of the article (link valid until October 05, 2018) https://authors.elsevier.com/a/1XZescFXJOkcW Process-based crop simulation models are often over-parameterised and are therefore difficult to calibrate properly. Following this rationale, the Morris screening sensitivity method was carried out on the DAISY model to identify the most influential input parameters operating on selected model outputs, i.e. crop yield, grain nitrogen (N), evapotranspiration and N leaching. The results obtained refer to the winter wheat-summer maize cropping system in the North China Plain. In this study, four different N fertiliser treatments over six years were considered based on a randomised field experiment at Luancheng Experimental Station to elucidate the impact of weather and nitrogen inputs on model sensitivity. A total of 128 parameters were considered for the sensitivity analysis. The ratios [output changes/parameter increments] demonstrated high standard deviations for the most relevant parameters, indicating high parameter non-linearity/interactions. In general, about 34 parameters influenced the outputs of the DAISY model for both crops. The most influential parameters depended on the output considered with sensitivity patterns consistent with the expected dominant processes. Interestingly, some parameters related to the previous crop were found to affect output variables of the following crop, illustrating the importance of considering crop sequences for model calibration. The developed RDAISY toolbox used in this study can serve as a basis for following sensitivity analysis of the DAISY model, thus enabling the selection of the most influential parameters to be considered with model calibration.
- Jul 2018
A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2‐6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters; average bias, model effect variance, environment effect variance and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models, and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations. This article is protected by copyright. All rights reserved.
- Jun 2018
Previous studies on water quality in China have been limited within a relatively constrained geographical area to interpret the relationships between water quality and its controlling factors. Such limited choice may give unreliable results, and a national study may help fill this gap. We first interpreted the dominant drivers controlling spatial patterns of surface water quality in the whole China at three scales (watershed, 1-km-buffer, and self-defined zone). The higher responsibility coefficients in spatial water quality models of urban and cropland than other lands imply that both urbanization and crop production have shown severe impact on surface water quality, accompanying with a dilution effect from rainfall at all spatial scales. Finally, a first exponential response of water quality measures to the scenarios of land use and land cover change had been originally demonstrated at the national level, suggesting higher sensitivity of clear water in some remote areas to potential pollutants, and a big challenge for improving the degraded water quality in some areas under intensive human activities. Our findings highlighted that it is urgent for decision-makers to make a scientific and reasonable macro-level policy for the sustainable development of water resources in China.
The Paris Agreement set a long-term temperature goal of holding the global average temperature increase to below 2.0 °C above pre-industrial levels, pursuing efforts to limit this to 1.5 °C; it is therefore important to understand the impacts of climate change under 1.5 and 2.0 °C warming scenarios for climate adaptation and mitigation. Here, climate scenarios from four global circulation models (GCMs) for the baseline (2006–2015), 1.5, and 2.0 °C warming scenarios (2106–2115) were used to drive the validated Variable Infiltration Capacity (VIC) hydrological model to investigate the impacts of global warming on runoff and terrestrial ecosystem water retention (TEWR) across China at a spatial resolution of 0.5°. This study applied ensemble projections from multiple GCMs to provide more comprehensive and robust results. The trends in annual mean temperature, precipitation, runoff, and TEWR were analyzed at the grid and basin scale. Results showed that median change in runoff ranged from 3.61 to 13.86 %, 4.20 to 17.89 %, and median change in TEWR ranged from −0.45 to 6.71 and −3.48 to 4.40 % in the 10 main basins in China under 1.5 and 2.0 °C warming scenarios, respectively, across all four GCMs. The interannual variability of runoff increased notably in areas where it was projected to increase, and the interannual variability increased notably from the 1.5 to the 2.0 °C warming scenario. In contrast, TEWR would remain relatively stable, the median change in standard deviation (SD) of TEWR ranged from −10 to 10 % in about 90 % grids under 1.5 and 2.0 °C warming scenarios, across all four GCMs. Both low and high runoff would increase under the two warming scenarios in most areas across China, with high runoff increasing more. The risks of low and high runoff events would be higher under the 2.0 than under the 1.5 °C warming scenario in terms of both extent and intensity. Runoff was significantly positively correlated to precipitation, while increase in maximum temperature would generally cause runoff to decrease through increasing evapotranspiration. Likewise, precipitation also played a dominant role in affecting TEWR. Our results were supported by previous studies. However, there existed large uncertainties in climate scenarios from different GCMs, which led to large uncertainties in impact assessment. The differences among the four GCMs were larger than differences between the two warming scenarios. Our findings on the spatiotemporal patterns of climate impacts and their shifts from the 1.5 to the 2.0 °C warming scenario are useful for water resource management under different warming scenarios.
A new temperature goal of holding the increase in global average temperature well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels has been established in the Paris Agreement, which calls for an understanding of climate risk under 1.5 and 2.0 °C warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e. maize, wheat, rice) in China during 2106–2115 in warming scenarios of 1.5 and 2.0 °C using a method of ensemble simulation with well-validated Model to capture the Crop–Weather relationship over a Large Area (MCWLA) family crop models, their 10 sets of optimal crop model parameters and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and the probability of crop yield decrease. Results showed that climate change would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the major cultivation areas, due to a decrease in crop growth duration and an increase in extreme events. By contrast, with moderate increases in temperature, solar radiation, precipitation and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resources could be ameliorated, which would enhance canopy photosynthesis and consequently biomass accumulations and yields. The moderate climate change would slightly worsen the maize growth environment but would result in a much more appropriate growth environment for wheat and rice. As a result, wheat, rice and maize yields would change by +3.9 (+8.6), +4.1 (+9.4) and +0.2 % (−1.7 %), respectively, in a warming scenario of 1.5 °C (2.0 °C). In general, the warming scenarios would bring more opportunities than risks for crop development and food security in China. Moreover, although the variability of crop yield would increase from 1.5 °C warming to 2.0 °C warming, the probability of a crop yield decrease would decrease. Our findings highlight that the 2.0 °C warming scenario would be more suitable for crop production in China, but more attention should be paid to the expected increase in extreme event impacts.
The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. Data access via doi 10.7910/DVN/M9ZT0F
Many studies have demonstrated the remarkable potential of assimilating remotely sensing leaf area index (LAI) products into crop models in estimating regional crop yield. To ensure the temporal consistency between crop models and remote-sensing system, it is prerequisite to derive the crop phenology information from the LAI products. However, previous studies mainly detected the phenology through the vegetation index (VI). Although some pieces of research applied LAI in phenology monitoring for trees and shrubs, fewer focused on crops, especially those with two or three growing seasons annually. Thus, which smoothing algorithm methods are suitable to obtain phenology of double-cropping rice and their difference in smoothing for crops are still unknown. Based on the Global Land Surface Satellite (GLASS)LAI products, we applied four favourite smoothing algorithms (Asymmetric Gaussian fitting, Double Logistic fitting, Savitzky–Golay filter, and Wavelet-based Filter method) to reduce noise and reconstruct the LAI profile and then detected the phenological information of double-cropping rice in Hunan Province. Compared with ground actual observations, we found that two fitting methods are not suitable to smooth double-cropping rice LAI, while the wavelet method performed the best. Based on the wavelet method, we estimated the phenological information of double-cropping rice at different regional scales as well and the results reflected that the accuracy of regional estimation is also acceptable. This study implied that the wavelet method is rather suitable to detect phenological information of crops from LAI products, which provides narrow gaps between two growing season. Our contribution can benefit researchers who focus on agriculture or remote sensing, especially those who would like to assimilate remotely sensed information into crop growth models.
China is facing the challenge of feeding a growing population with the declining cropland and increasing shortage of water resources under the changing climate. This study identified that the opportunistic profit‐driven shifts of planting areas and crop species composition have strongly reduced the food production capacity of China. First, the regional cultivation patterns of major crops in China have substantially shifted during the past five decades. Southeast and South China, the regions with abundant water resources and fewer natural disasters, have lost large planting areas of cropland in order to pursue industry and commerce. Meanwhile, Northeast and Northwest China, the regions with low water resources and frequent natural disasters, have witnessed increases in planting areas. These macroshifts have reduced the national food production by 1.02% per year. The lost grain production would have been enough to feed 13 million people. Second, the spatial shifts have been accompanied by major changes in crop species composition, with substantial increases in planting area and production of maize, due to its low water consumption and high economic returns. Consequently, the stockpile of maize in China has accounted for more than half of global stockpile, and the stock to use ratio of maize in China has exceeded the reliable level. Market‐driven regional shifts of cropping practices have resulted in larger irrigation requirements and aggravated environmental stresses. Our results highlighted the need for Chinese food policies to consider the spatial shifts in cultivation, and the planting crop compositions limited by regional water resources and climate change. Substantial shifts in the regional cultivation patterns have occurred in China Macroshifts have reduced food production by 1.02% nationally each year The spatial shifts were accompanied by major changes in crop species composition
Crop production in northern regions is projected to benefit from longer growing seasons brought on by future climate change. However, production also faces multiple challenges due to more frequent and intense extreme weather phenomena, and uncertain future prices of agricultural inputs and outputs. Extensive studies have been conducted to investigate the impacts of climate change on cereals yield change, but integrated assessments that also consider the management and economy of cereal farms have been rare so far. In this study, the effects of climate change-driven crop productivity change on farm level land use dynamics, input use, production management and farm income were considered from the point of view of dynamic decision making of a rational risk averse farmer. We assessed whether a farmer can gain from improved crop yields when using adapted cultivars and managing the farm accordingly. We incorporated crop yield estimates from a process-based large area crop model (MCWLA) run with two climate scenarios into a dynamic economic model of farm management and crop rotation (DEMCROP) to investigate future input use, land use with crop rotation, economic gross margins and greenhouse gas emissions. A time span of 30 years was considered. The model accounts for the yield responses to fertilisation, crop protection, liming of field parcels, and yield losses due to monoculture. The approach resulted in a novel and necessary analysis of farm management, production and income implications of climate change adaptation under different climate and socio-economic scenarios. We analysed the effects of different climate and price scenarios at a typical cereal farm in the North Savo region, which is currently a marginal area for crop production in Finland due to its harsh climate. Crop modelling results suggest a 19–27% increase of spring cereal yields and 11–19% increase of winter wheat yields from the current level until 2042–2070. According to our economic farm level simulations, these yield increases would incentivise farmers towards more intense input use resulting in additional increase of yields by 3–8% at current prices. More land is allocated to barley and wheat, less to set-aside and oat. The economic gross margin would increase significantly from the current low levels. Greenhouse gas emissions from farms were estimated to increase with increasing production, but emissions per quantity produced (measured as feed energy units) would decrease. There is potential for sustainable intensification (SI) of crop production in the region.
Global freshwater resources are under increasing pressure. It is reported that international trade of water-intensive products (the so-called virtual water trade) can be used to ease global water pressure. In spite of the significant amount of international trade of woody forest products, virtual water of woody forest products (VWWFP) and the corresponding international trade are largely ignored. However, virtual water research has progressed steadily. This study maps VWWFP and statistically analyzes China’s official data for the period 1993–2014. The results show a rapid increase in the trend of VWWFP flow from China, reaching 7.61 × 1012 m3 or 3.48 times annual virtual water trade for agricultural products. The export and import volumes of China are respectively 1.27 × 1012 m3 and 6.34 × 1012 m3 for 1993–2014. China imported a total of 5.07 × 1012 m3 of VWWFP in 1993–2014 to lessen domestic water pressure, which is five times the annual water transfer via China’s South–North Water Transfer project. Asia and Europe account for the highest contribution (50.52%) to China’s import. Other contributors include the Russian Federation (16.63%), Indonesia (13.45%), Canada (13.41%), the United States of America (9.60%), Brazil (7.23%) and Malaysia (6.33%). China mainly exports VWWFP to Asia (47.68%), North America (23.24%), and Europe (20.01%). The countries which export the highest amount of VWWFP include the United States of America, Japan, Republic of Korea and Canada. Then the countries which import the highest amount of VWWFP include the Russian Federation, Canada, United States of America, and Brazil. The VWWFP flow study shows an obvious geographical distribution that is driven by proximity and traffic since transportation cost of woody forest products could be significant.
A new temperature goal of holding the increase in global average temperature well below 2℃ above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ℃ above pre-industrial levels has been established in Paris Agreement, which calls for understanding of climate risk under 1.5 ℃ & 2.0 ℃ warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e., maize, wheat, rice) in China during 2106–2115 at warming scenarios of 1.5 ℃ & 2.0 ℃ using the method of ensemble simulation with well-validated MCWLA family crop models, their 10 sets of optimal crop model parameters, and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and probability of crop yield decrease. Results showed that the decrease of crop growth duration and the increase of extreme events impacts in the future would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the cultivation areas. By contrast, with the moderate increases in temperature, solar radiation, precipitation, and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resource could be ameliorated which enhance canopy photosynthesis, and consequently biomass accumulations and yields. The moderate climate change would slightly deteriorate maize growth environment but result in much more appropriate growth environment for wheat and rice. As a result, the wheat and rice yields could increase by 3.9 % and 4.1 %, respectively, and maize yield could increase by 0.2 %, at a warming scenario of 1.5 ℃. At the warming scenario of 2.0 ℃, wheat and rice yield would increase by 8.6 % and 9.4 %, respectively, but maize yield could decrease by 1.7 %. In general, the warming scenarios would bring more opportunities than risks for the crop development and food security in China. Moreover, although variability of crop yield would increase with the change of climate scenario from 1.5 ℃ warming to 2.0 ℃ warming, the probability of crop yield decrease would decrease. Our findings highlight that the 2.0 ℃ warming scenario would be more suitable for crop production in China, but the expected increase in extreme events impacts should be paid more attention to.
This work was financially supported by the Spanish National Institute for Agricultural and Food Research and Technology (INIA, MACSUR01-UPM), the Italian Ministry of Agriculture and Forestry and the Finnish Ministry of Agriculture and Forestry (D.M. 24064/7303/15) through FACCE MACSUR − Modelling European Agriculture with Climate Change for Food Security, a FACCE JPI knowledge hub; MULCLIVAR, from the Spanish Ministerio de Economía y Competitividad (MINECO, CGL2012-38923-C02-02); the Academy of Finland (decisions: 277276 and 277403), the EU FP7 IMPRESSIONS project (grant agreement no. 603416), the NORFASYS project (decision nos. 268277 and 292944) and PLUMES project (decision nos. 277403 and 292836); project IGA AF MENDELU no. 7/2015 with the support of the Specific University Research Grant provided by the Ministry of Education, Youth Sports of the Czech Republic; the Ministry of Education, Youth Sports of the Czech Republic within the National Sustainability Programme I (NPU I), grant number LO1415 NAZV QJ1310123 the Polish National Centre for Research and Development in frame of the projects: LCAgri, contract number BIOSTRATEG1/271322/3/NCBR/2015 and GyroScan, contract number BIOSTRATEG2/298782/11/NCBR/2016.
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9°C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses.The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description.Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index.Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters, and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters, and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate-change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources. This article is protected by copyright. All rights reserved.
- Dec 2017
Climate change and human activities are two major factors affecting water resource change. It is important to understand the roles of the major factors in affecting runoff change in different basins for watershed management. Here, we investigated the trends in climate and runoff in seven typical catchments in seven basins across China from 1961 to 2014. Then we attributed the runoff change to climate change and human activities in each catchment and in three time periods (1980s, 1990s and 2000s), using the VIC model and long-term runoff observation data. During 1961–2014, temperature increased significantly, while the trends in precipitation were insignificant in most of the catchments and inconsistent among the catchments. The runoff in most of the catchments showed a decreasing trend except the Yingluoxia catchment in the northwestern China. The contributions of climate change and human activities to runoff change varied in different catchments and time periods. In the 1980s, climate change contributed more to runoff change than human activities, which was 84%, 59%, − 66%, − 50%, 59%, 94%, and − 59% in the Nianzishan, Yingluoxia, Xiahui, Yangjiaping, Sanjiangkou, Xixian, and Changle catchment, respectively. After that, human activities had played a more essential role in runoff change. In the 1990s and 2000s, human activities contributed more to runoff change than in the 1980s. The contribution by human activities accounted for 84%, − 68%, and 67% in the Yingluoxia, Xiahui, and Sanjiangkou catchment, respectively, in the 1990s; and − 96%, − 67%, − 94%, and − 142% in the Nianzishan, Yangjiaping, Xixian, and Changle catchment, respectively, in the 2000s. It is also noted that after 2000 human activities caused decrease in runoff in all catchments except the Yingluoxia. Our findings highlight that the effects of human activities, such as increase in water withdrawal, land use/cover change, operation of dams and reservoirs, should be well managed.
The Paris Agreement set a long-term temperature goal of holding the global average temperature increase to below 2.0 ℃ above pre-industrial levels, and pursuing efforts to limit this to 1.5 ℃, it is therefore important to understand the impacts of climate change under 1.5 ℃ and 2.0 ℃ warming scenarios for climate adaptation and mitigation. Here, climate scenarios by four Global Circulation Models (GCMs) for the baseline (2006–2015), 1.5 ℃ and 2.0 ℃ warming scenarios (2106–2115) were used to drive the validated Variable Infiltration Capacity (VIC) hydrological model to investigate the impacts of global warming on river runoff and Terrestrial Ecosystem Water Retention (TEWR) in China. The trends in annual mean temperature, precipitation, river runoff and TEWR were analysed at the grid and basin scale. Results showed that there were large uncertainties in climate scenarios from the different GCMs, which led to large uncertainties in the impact assessment. The differences among the four GCMs were larger than differences between the two warming scenarios. The interannual variability of river runoff increased notably in areas where it was projected to increase, and the interannual variability increased notably from 1.5 ℃ warming scenario to 2.0 ℃ warming scenario. By contrast, TEWR would remain relatively stable. Both extreme low and high river runoff would increase under the two warming scenarios in most areas in China, with high river runoff increasing more. And the risk of extreme river runoff events would be higher under 2.0 ℃ warming scenario than under 1.5 ℃ warming scenario in term of both extent and intensity. River runoff was significantly positively correlated to precipitation, while increase in maximum temperature would generally cause river runoff to decrease through increasing evapotranspiration. Likewise, precipitation also played a dominant role in affecting TEWR. Our findings highlight climate change mitigation and adaptation should be taken to reduce the risks of hydrological extreme events.
The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.
- Oct 2017
In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low.
- Sep 2017
Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.
Agricultural production systems are facing the challenges of increasing food production while reducing environmental cost, particularly in China. To improve yield potential and eco-efficiency simultaneously for the rice-wheat rotation system in China, we investigated changes in potential yields and yield gaps based on the field experiment data from 1981 to 2009 at four representative agro-meteorological experiment stations, along with the Agricultural Production System Simulator (APSIM) rice-wheat model. We further optimized crop cultivar and sowing/transplanting date, and investigated crop yield, water and nitrogen use efficiency, and environment impact of the rice-wheat rotation system in response to water and nitrogen supply. We found that the yield gaps between potential yields and farmer’s yields were about 8101 kg/ha or 45.3% of the potential yield, which had been shrinking from 1981 to 2009. To improve yield potentials and eco-efficiency, the cultivars of rice and wheat that properly increase both radiation use efficiency and grain weight are promising. Rice cultivars breeding need to maintain the length of panicle development and reproductive phase. High-yielding wheat cultivars are characterized by medium vernalization sensitivity, low photoperiod sensitivity and short length of floral initiation phase. Proper shift in sowing date can alleviate the negative effect of climate risk. Intermittent irrigation scheme (irrigate until surface soil saturated when average water content of surface soil is <50% of saturated water content) for rice, together with nitrogen application rate of 390–420 kg N/ha (180–210 kg N/ha for rice and 210 kg N/ha for wheat), is suggested for the rice-wheat rotation system to maintain high yield with high resource use efficiency. This suggested nitrogen application rates are lower than those currently used by many local farmers. Our findings are useful to improve yield potential and eco-efficiency for the rice-wheat rotation system in China. Furthermore, this study demonstrates an effective approach with crop modelling to design farming system for sustainable intensification, which can be adapted to other farming systems and regions.
Response and feedback of land surface process to climate change is one of the research priorities in the field of geoscience. The current study paid more attention to the impacts of global change on land surface process, but the feedback of land surface process to climate change has been poorly understood. It is becoming more and more meaningful under the framework of Earth system science to understand systematically the relationships between agricultural phenology dynamic and biophysical process, as well as the feedback on climate. In this paper, we summarized the research progress in this field, including the fact of agricultural phenology change, parameterization of phenology dynamic in land surface progress model, the influence of agricultural phenology dynamic on biophysical process, as well as its feedback on climate. The results showed that the agriculture phenophase, represented by the key phenological phases such as sowing, flowering and maturity, had shifted significantly due to the impacts of climate change and agronomic management. The digital expressions of land surface dynamic process, as well as the biophysical process and atmospheric process, were improved by coupling phenology dynamic in land surface model. The agricultural phenology dynamic had influenced net radiation, latent heat, sensible heat, albedo, temperature, precipitation, circulation, playing an important role in the surface energy partitioning and climate feedback. Considering the importance of agricultural phenology dynamic in land surface biophysical process and climate feedback, the following research priorities should be stressed: (1) the interactions between climate change and land surface phenology dynamic; (2) the relations between agricultural phenology dynamic and land surface reflectivity at different spectrums; (3) the contributions of crop physiology characteristic changes to land surface biophysical process; (4) the regional differences of climate feedbacks from phenology dynamic in different climate zones. This review is helpful to accelerate understanding of the role of agricultural phenology dynamic in land surface process and climate feedback.
Vapor pressure deficit (VPD) is a widely used measure of atmospheric water demand. It is closely related to crop evapotranspiration and consequently has major impacts on crop growth and yields. Most previous studies have focused on the impacts of temperature, precipitation, and solar radiation on crop yields, but the impact of VPD is poorly understood. Here, we investigated the spatial and temporal changes in VPD and their impacts on yields of major crops in China from 1980 to 2008. The results showed that VPD during the growing period of rice, maize, and soybean increased by more than 0.10 kPa (10 yr)–1 in northeastern and southeastern China, although it increased the least during the wheat growing period. Increases in VPD had different impacts on yields for different crops and in different regions. Crop yields generally decreased due to increased VPD, except for wheat in southeastern China. Maize yield was sensitive to VPD in more counties than other crops. Soybean was the most sensitive and rice was the least sensitive to VPD among the major crops. In the past three decades, due to the rising trend in VPD, wheat, maize, and soybean yields declined by more than 10.0% in parts of northeastern China and the North China Plain, while rice yields were little affected. For China as a whole, the trend in VPD during 1980–2008 increased rice yields by 1.32%, but reduced wheat, maize, and soybean yields by 6.02%, 3.19%, and 7.07%, respectively. Maize and soybean in the arid and semi-arid regions in northern China were more sensitive to the increase in VPD. These findings highlight that climate change can affect crop growth and yield through increasing VPD, and water-saving technologies and agronomic management need to be strongly encouraged to adapt to ongoing climate change.
Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.
Rice models have been widely used in simulating and predicting rice phenology in contrasting climate zones, however the uncertainties from model structure (different equations or models) and/or model parameters were rarely investigated. Here, five rice phenological models/modules (i.e., CERES-Rice, ORYZA2000, RCM, Beta Model and SIMRIW) were applied to simulate rice phenology at 23 experimental stations from 1992 to 2009 in two major rice cultivation regions of China: the northeastern China and the southwestern China. To investigate the uncertainties from model biophysical parameters, each model was run with randomly perturbed 50 sets of parameters. The results showed that the median of ensemble simulations were better than the simulation by most models. Models couldn’t simulate well in some specific years despite of parameters optimization, suggesting model structure limit model performance in some cases. The models adopting accumulative thermal time function (e.g., CERES-Rice and ORYZA2000) had better performance in the southwestern China, in contrast, those adopting exponential function (e.g., Beta model and RCM model) had better performance in the northeastern China. In northeastern China, the contribution of model structure and model parameters to model total variance was, respectively, about 55.90% and 44.10% in simulating heading date, and about 75.43% and 24.57% in simulating maturity date. In the southwestern China, the contribution of model structure and model parameters to model total variance was, respectively, about 79.97% and 27.03% in simulating heading date, about 92.15% and 7.85% in simulating maturity date. Uncertainty from model structure was the most relevant source. The results highlight that the temperature response functions of rice development rate under extreme climate conditions should be improved based on environment-controlled experimental data.
- Jul 2017
Impact of high temperature stress on crop growth and productivity is one key concern with respect to crop production and food security under climate change. Due to the complexity and diversity of crop characteristics and farmers’ management practices, as well as the difficulties in quantifying those agronomic management practices at reasonable temporal and spatial scales, crop responses to heat stress at a regional scale have not been properly assessed yet. In this study, we used remote-sensing data to investigate the responses of growth duration and leaf area index (LAI) of winter wheat to extreme high temperature during reproductive growing stage in the North China Plain from 2001 to 2008. Growing degree days above 0 °C (GDD) from heading to maturity was used to represent average temperature of growing environment, and the extreme temperature (>34 °C) degree days (EDD) was used as an indicator for heat stress. We detected statistically significant shortening of reproductive growing duration due to increase in GDD and EDD at both site and regional scales. We also found acceleration of leaf senescence under warmer environment, as well as considerable damages to leaf area by extremely high temperatures according to LAI values from remote-sensing data. Our results present the explicit patterns of crop responses to heat stress at different spatial scales and periods, indicating the complexity of the impacts of extreme events. Moreover, we highlighted that exposure, vulnerability and adaptation all should be considered in evaluating the impacts of extreme events. In addition, our findings suggest great potential for improving regional crop growth monitoring and yield prediction through assimilating remote-sensing data into mechanistic crop simulation models.
Rice in China is increasingly suffered from extreme temperature stress (ETS) with ongoing climate change. It is projected that ETS would increase notably across the world in the future. However, the spatio-temporal change of ETS in main rice planting areas in China is still unclear; and the future yield loss caused by ETS (YLETS) has seldom been investigated quantitatively. In this study, we first investigated the spatio-temporal change of ETS across China under 20 climate change scenarios consisting of five global climate models and four Representative Carbon Pathways (2.6, 4.5, 6.0, and 8.5). Then, using a process-based crop model (MCWLA-Rice), its 30 sets of model parameters and the 20 climate change scenarios, we conducted a super-ensemble assessment to investigate the YLETS over 2020-2049, relative to the baseline period (1980-2009), across China. The results showed that, an increased heat ETS and a decreased cold ETS would be expected for most areas. As a result, a large spatial variability of yield loss would be expected in the future, including severely cold stress for region I (northeastern China, single rice) and region IV (southern China, early rice), but severely heat stress for region III (the middle and lower reaches of Yangtze River, single rice) and region IV (southern China, late rice). Comparing yield loss from both ETS, a decreased change in yield loss would be mainly expected in region I (northeastern China, single rice), while an increased change for region III (the middle and lower reaches of Yangtze River, single rice) and IV (southern China, late rice), with less change for region II (southwestern China, single rice) and IV (southern China, early rice). Finally, some adaptation measures were proposed for the ETS-sensitive areas. Our findings are useful to develop effective policies to cope with climate risk and relieve ETS disasters.
Increasing population, limited land resources, and the demand for environmental protection highlight the urgency of improving crop yield on the limited cultivated land. To identify a possible food supply under a sustainable intensification of agricultural production, it is necessary to accurately estimate yield potentials and yield gaps. Here, we used a well-validated, large-scale process-based crop model, Model to capture the Crop-weather relationship over a Large Area for rice, and an ensemble model simulation method to estimate the yield potential across the rice planting area of China. We further evaluated the spatiotemporal patterns of actual yield, yield potential, and yield gap over the past three decades. Rice yield showed an increasing trend in more than 95% of the studied counties. However, 48.3% of the counties were already experiencing yield stagnation. The yield potential in northeast China had increased over the past three decades by 20–40 kg/ha per year because of the increase in temperature, while the increased temperature and decreased solar radiation reduced the yield potentials in other regions by 10–30 kg/ha per year. Because of changes in the actual yield and yield potential, the yield gap decreased in 93.4% of the counties by an average of 0.5–2% per year, resulting in less room for yield improvement. Additionally, 65.9% of the counties had nearly approached their yield ceilings (>70% of the yield potential). Our study highlights that popularizing advanced management technologies to close yield gaps and breeding climate-resilient cultivars to expand yield potentials should be of equal importance for the sustainable development of agricultural production and food security.
- Apr 2017
Provision of food security in the face of increasing global food demand requires narrowing of the gap between actual farmer's yield and maximum attainable yield. So far, assessments of yield gaps have focused on average yield over 5-10. years, but yield gaps can vary substantially between crop seasons. In this study we hypothesized that climate-induced inter-annual yield variability and associated risk is a major barrier for farmers to invest, i.e. increase inputs to narrow the yield gap.We evaluated the importance of inter-annual attainable yield variability for the magnitude of the yield gap by utilizing data for wheat and maize at ten sites representing some major food production systems and a large range of climate and soil conditions across the world. Yield gaps were derived from the difference of simulated attainable yields and regional recorded farmer yields for 1981 to 2010. The size of the yield gap did not correlate with the amplitude of attainable yield variability at a site, but was rather associated with the level of available resources such as labor, fertilizer and plant protection inputs. For the sites in Africa, recorded yield reached only 20% of the attainable yield, while for European, Asian and North American sites it was 56-84%. Most sites showed that the higher the attainable yield of a specific season the larger was the yield gap. This significant relationship indicated that farmers were not able to take advantage of favorable seasonal weather conditions. To reduce yield gaps in the different environments, reliable seasonal weather forecasts would be required to allow farmers to manage each seasonal potential, i.e. overcoming season-specific yield limitations.
In this study, two categories of weather index—absolute index and relative index—for chilling injury and heat damage of three main crops in China were assessed to identify insurable counties. First, correlations between selected weather indices and yield losses were examined for each county. If a correlation was significant, the county was categorized as “insurable” for the corresponding hazard or index. Second, the spatial distribution of insurable counties was characterized and finally, their correlation coefficients were analyzed at various spatial scales. The results show that the spatial patterns of insurable areas varied by categories of weather indices, crops, and hazards. Moreover, the weather indices based on relative threshold of temperature were more suitable for chilling injury in most regions, whereas the indices based on absolute threshold were more suitable for heat damage. The findings could help the Chinese government and insurance companies to design effective insurance products.
This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thünen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/-1Mgha⁻¹ around the mean. The bias of the median of the 21 models was less than 1Mgha⁻¹. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.
- Feb 2017
Wetland is one of the most important ecosystems, and it has high social benefit, economic benefit and scientific research value. However, wetland resources are bearing a heavy pressure because of various natural and anthropogenic factors. The degradation of the wetland quality and quantity has aroused widespread concerns. To conserve and manage wetland resources, it is important to monitor wetlands and their adjacent uplands. Satellite remote sensing has several advantages, such as wild coverage, saving time and labor, multi-temporal, multi-platform, containing a large amount of information, and so on, when monitoring wetland resources especially in large geographic areas. In early work, the satellite imagery used the visual interpretation for classification, which is still used widely today. The most commonly used computer classification methods are unsupervised classification and supervised classification. However, it is difficult to make great progress on improving the accuracy of remote sensing classification because of "different things with the same spectrums" in wetlands. Spectrum confusion among wetlands seriously restricts the extraction of wetland information and the application of remote sensing technology in the monitoring of the wetland. But the traditional pixel-based methods cannot overcome this difficulty because it only used the spectral features of imagery, ignoring other information that the remote sensing imagery carries, although it has been universally applied in land cover information extraction for many years. In order to over this difficulty and promote the application of remote sensing technology in dynamic monitoring of wetland, a new hybrid classification approach for wetland was proposed in this paper, which combined the object-oriented technology and the tasseled cap transformation method. The new proposed approach was further checked by a case study of wetland extraction based on the HJ-CCD and Landsat ETM (enhanced thematic mapper) remote sensing images in 2010 in the eastern Dongting Lake region. We yielded a better classification result using the new approach. The overall accuracy was 90.02% and the Kappa coefficient was 0.88, which were much higher than that of the traditional pixel-based methods. Meanwhile, this method significantly reduced the disturbance of salt-and-pepper noise, and the results were quite compact and smooth compared with that using other traditional classification methods. A higher accuracy was obtained for the proposed approach for vegetation wetlands including wood wetland, shrub wetland and grass wetland, which was attributed to the full mining of imagery spectral information through the tasseled cap transformation. The accuracy of the hybrid approach was much higher than that of others for river, channel, reservoir and lake whose spectrums were extremely similar. This was mainly because the object-oriented technology could fully utilize spatial and shape information of imagery. Hence, according to the experiment results, the proposed approach combing the object-oriented technology and the tasseled cap transformation is an effective method in wetland extraction using the remote sensing technology and can overcome the difficulty of spectrum similarity, which is mainly attributed to making full use of spatial feature on the basis of exploring the spectral features through the tasseled cap transformation. Meanwhile, we can conclude that Chinese HJ-CCD images are an important data source for monitoring the dynamics of wetland. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
Martre P, Reynolds MP, Asseng S, Ewert F, Alderman PD, Cammarano D, Maiorano A, Ruane AC, Aggarwal PK, Anothai J, Basso B, Biernath C, Challinor AJ, De Sanctis G, Doltra J, Dumont B, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kassie BT, Kersebaum KC, Koehler AK, Müller C, Kumar SN, Liu B, Lobell DB, Nendel C, O’Leary G, Olesen JE, Palosuo T, Priesack E, Eyshi Rezaei E, Ripoche D, Rötter RP, Semenov MA, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn P, Waha K, Wang E, White JW, Wolf J, Zhao Z, and Zhu Y
Air pollution and climate change are increasing threats to agricultural production and food security. Extensive studies have focused on the effect of climate change, but the interactive effects of multiple global change factors are poorly understood. Here, we incorporate the interactions between climate change, carbon dioxide (CO2) and ozone (O3) into an eco-physiological mechanistic model based on three years of O3 Free-Air Concentration Elevation (O3-FACE) experiments. We then investigate the effects of climate change, elevated CO2 concentration ([CO2]) and rising O3 concentration ([O3]) on wheat growth and productivity in eastern China in 1996–2005 (2000s) and 2016–2025 (2020s) under two climate change scenarios, singly and in combination. We find the interactive effects of climate change, CO2 and O3 on wheat productivity have spatially explicit patterns; the effect of climate change dominates the general pattern, which is however subject to the large uncertainties of climate change scenarios. Wheat productivity is estimated to increase by 2.8–9.0% due to elevated [CO2] however decline by 2.8–11.7% due to rising [O3] in the 2020s, relative to the 2000s. The combined effects of CO2 and O3 are less than that of O3 only, on average by 4.6–5.2%, however with O3 damage outweighing CO2 benefit in most of the region. This study demonstrates a more biologically meaningful and appropriate approach for assessing the interactive effects of climate change, CO2 and O3 on crop growth and productivity. Our findings promote the understanding on the interactive effects of multiple global change factors across contrasting climate conditions, cast doubt on the potential of CO2 fertilization effect in offsetting possible negative effect of climate change on crop productivity as suggested by many previous studies.
- Jan 2017
- The Geographical Sciences During 1986—2015
Detection and attribution are essential to understand the roles of natural and anthropogenic factors in changes of land surface sensitive components such as terrestrial ecosystem, water resources and crop yield. In this chapter, we review the research progress on detection and attribution using the literature statistics and analyses based on 240 international SCI/SSCI journals in the related fields such as geographical sciences, ecology, atmospheric science, hydrology and agriculture. We also review the research progress on detection and attribution in China. We conclude that Chinese scientists have had great progresses on detection and attribution in the past three decades; nevertheless more novel studies are needed to obtain high quality observation data on land surface dynamics and develop human and natural systems coupled models.
Climate change and its associated higher frequency and severity of adverse weather events require genotypic adaptation. Process-based ecophysiological modelling offers a powerful means to better target and accelerate development of new crop cultivars. Barley (Hordeum vulgare L.) is an important crop throughout the world, and a good model for study of the genetics of stress adaptation because many quantitative trait loci and candidate genes for biotic and abiotic stress tolerance have been identified in it. Here, we developed a new approach to design future crop ideotypes using an ensemble of eight barley simulation models (i.e. APSIM, CropSyst, HERMES, MCWLA, MONICA, SIMPLACE, SiriusQuality, and WOFOST), and applied it to design climate-resilient barley ideotypes for Boreal and Mediterranean climatic zones in Europe. The results showed that specific barley genotypes, represented by sets of cultivar parameters in the crop models, could be promising under future climate change conditions, resulting in increased yields and low inter-annual yield variability. In contrast, other genotypes could result in substantial yield declines. The most favorable climate-zone-specific barley ideotypes were further proposed, having combinations of several key genetic traits in terms of phenology, leaf growth, photosynthesis, drought tolerance, and grain formation. For both Boreal and Mediterranean climatic zones, barley ideotypes under future climatic conditions should have a longer reproductive growing period, lower leaf senescence rate, larger radiation use efficiency or maximum assimilation rate, and higher drought tolerance. Such characteristics can produce substantial positive impacts on yields under contrasting conditions. Moreover, barley ideotypes should have a low photoperiod and high vernalization sensitivity for the Boreal climatic zone; for the Mediterranean, in contrast, it should have a low photoperiod and low vernalization sensitivity. The drought-tolerance trait is more beneficial for the Mediterranean than for the Boreal climatic zone. Our study demonstrates a sound approach to design future barley ideotypes based on an ensemble of well-tested, diverse crop models and on integration of knowledge from multiple disciplines. The robustness of model-aided ideotypes design can be further enhanced by continuously improving crop models and enhancing information exchange between modellers, agro-meteorologists, geneticists, physiologists, and plant breeders.
- Jan 2017
In four chapters and an introduction, this book systematically helps readers understand the development of the Geographical Sciences both in China and in the world during the past 30 years. Through data analysis of methodologies including CiteSpace, TDA, qualitative analysis, questionnaires, data mining and mathematical statistics, the book explains the evolution of research topics and their driving factors in the Geographical Sciences and its four branches, namely Physical Geography, Human Geography, Geographical Information Science and Environmental Geography. It also identifies the role of the Geographical Sciences in the analysis of strategic issues such as global change and terrestrial ecosystems, terrestrial water cycle and water resources, land change, global cryosphere evolution and land surface processes on the Tibetan Plateau, economic globalization and local responses, regional sustainable development, remote sensing modelling and parameter inversion, spatial analysis and simulation, and tempo-spatial processes and modelling of environmental pollutants. It then discusses research development and inadequacy of Chinese Geographical Sciences in the above-mentioned topics, as well as in the fields including Geomorphology and Quaternary environmental change, Ecohydrology, ecosystem services, the urbanization process and mechanism, medical and health geography, international rivers and transboundary environment and resources, detection and attribution of changes in land surface sensitive components, and uncertainty of spatial information and spatial analysis. It shows that the NSFC has driven the development in all these topics and fields. In addition, the book summarises trends of the Geographical Sciences in China and the research level in major countries of the world through an overview of geographical education in colleges and universities, the analysis of publications, citations and author networks of SCI/SSCI and CSCD indexed articles, and the description of Sino-USA, Sino-UK and Sino-German cooperation. This book serves as an important reference to anyone interested in geographical sciences and related fields.
- Nov 2016
Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.
Increasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson’s r < 0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1–5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield (r > 0.40, p < 0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts.
The contributions of climate and land use change (LUCC) to hydrological change in Heihe River Basin (HRB), Northwest China were quantified using detailed climatic, land use and hydrological data, along with the process-based SWAT (Soil and Water Assessment Tool) hydrological model. The results showed that for the 1980s, the changes in the basin hydrological change were due more to LUCC (74.5%) than to climate change (21.3%). While LUCC accounted for 60.7% of the changes in the basin hydrological change in the 1990s, climate change explained 57.3% of that change. For the 2000s, climate change contributed 57.7% to hydrological change in the HRB and LUCC contributed to the remaining 42.0%. Spatially, climate had the largest effect on the hydrology in the upstream region of HRB, contributing 55.8%, 61.0% and 92.7% in the 1980s, 1990s and 2000s, respectively. LUCC had the largest effect on the hydrology in the middle-stream region of HRB, contributing 92.3%, 79.4% and 92.8% in the 1980s, 1990s and 2000s, respectively. Interestingly, the contribution of LUCC to hydrological change in the upstream, middle-stream and downstream regions and the entire HRB declined continually over the past 30 years. This was the complete reverse (a sharp increase) of the contribution of climate change to hydrological change in HRB.
For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1℃ global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify 'method uncertainty' in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
- Aug 2016
Distinct climate changes since the end of the 1980s have led to clear responses in crop phenology in many parts of the world. This study investigated the trends in the dates of spring wheat phenology in relation to mean temperature for different growth stages. It also analyzed the impacts of climate change, cultivar shift, and sowing date adjustments on phenological events/phases of spring wheat in northern China (NC). The results showed that significant changes have occurred in spring wheat phenology in NC due to climate warming in the past 30 years. Specifically, the dates of anthesis and maturity of spring wheat advanced on average by 1.8 and 1.7 day (10 yr)⁻¹. Moreover, while the vegetative growth period (VGP) shortened at most stations, the reproductive growth period (RGP) prolonged slightly at half of the investigated stations. As a result, the whole growth period (WGP) of spring wheat shortened at most stations. The findings from the Agricultural Production Systems Simulator (APSIM)-Wheat model simulated results for six representative stations further suggested that temperature rise generally shortened the spring wheat growth period in NC. Although the warming trend shortened the lengths of VGP, RGP, and WGP, the shift of new cultivars with high accumulated temperature requirements, to some extent, mitigated and adapted to the ongoing climate change. Furthermore, shifts in sowing date exerted significant impacts on the phenology of spring wheat. Generally, an advanced sowing date was able to lower the rise in mean temperature during the different growth stages (i.e., VGP, RGP, and WGP) of spring wheat. As a result, the lengths of the growth stages should be prolonged. Both measures (cultivar shift and sowing date adjustments) could be vital adaptation strategies of spring wheat to a warming climate, with potentially beneficial effects in terms of productivity. © 2016, The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg.
We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.
- Jul 2016
Changes in potential evapotranspiration and surface runoff can have profound implications for hydrological processes in arid and semiarid regions. In this study, we investigated the response of hydrological processes to climate change in Upper Heihe River Basin (UHRB) in Northwest China for the period from 1981 to 2010. We used agronomic, climatic and hydrological data to drive the SWAT (Soil and Water Assessment Tool) model for changes in potential evapotranspiration (ET0) and surface runoff and the driving factors in the study area. The results showed that increasing autumn temperature increased snow melt, resulting in increased surface runoff, especially in September and October. The spatial distribution of annual runoff was different from that of seasonal runoff, with the highest runoff in Yeniugou River, followed by Babaohe River and then the tributaries in the northern of the basin. There was no evaporation paradox at annual and seasonal time scales, and annual ET0 was driven mainly by wind speed. ET0 was driven by relative humidity in spring, sunshine hour duration in autumn, and both sunshine hour duration and relative humility in summer. Surface runoff was controlled by temperature in spring and winter and by precipitation in summer (flood season). Although surface runoff increased in autumn with increasing temperature, it depended on rainfall in September and on temperature in October and November. This article is protected by copyright. All rights reserved.
Climate variation will affect hydrological cycle, as well as the availability of water resources. In spite of large progresses have been made in the dynamics of hydrological cycle variables, the dynamics and drivers of blue water flow, green water flow and total flow (three flows), as well as the proportion of green water (GWC), in the past and future at county scale, were scarcely investigated. In this study, taking the Heihe River basin in China as an example, we investigated the dynamics of green and blue water flows and their controlling factors during 1980–2009 using five statistical approaches and the Soil and Water Assessment Tool (SWAT). We found that there were large variations in the dynamics of green and blue water flows during 1980–2009 at the county scale. Three flows in all counties showed an increasing trend except Jiayuguan and Jianta county. The GWC showed a downward trend in the Qilian, Suzhou, Shandan, Linze and Gaotai counties, but an upward trend in the Mingle, Sunan, Jinta, Jiayuguan, Ganzhou and Ejilaqi counties. In all the counties, the three flows and GWC had strong persistent trends in the future, which are mainly ascribed to rainfall variation. In the Qilian and Shandan counties, rainfall was the major controlling factor for the three flows and GWC. Rainfall controlled the green water and total flows in the Mingle, Linze and Gaotai counties; green water flow and GWC in the Suzhou county; green water flow, total flow and GWC in the Jinta and Ejilaqi counties. Our results also showed that the "Heihe River basin allocation project" had significant influences on the abrupt changes of the flows above-mentioned. Our results illustrate the status of the water resources at county scale, providing a reference for water resources management of inland river basins.
- Jun 2016
We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.
- May 2016
Virtual water has become an important part of global water supply and demand and has led to the globalization of water. Virtual water research most mainly focused on the field of agriculture. Minimal attention has been devoted to forest virtual water (FVW). To our knowledge, no research on the monitoring and analysis of FVW through remote-sensing technology has been conducted. In this study, based on object-oriented technology and through the use of 30 scenes from multi-temporal Chinese HJ-CCD images, we monitored FVW in Hunan Province, China, in 2010 and analysed the pattern of FVW. Results showed that the amount of FVW is large and greater than that of entity water. Hunan Province had 5.83 × 1011m3 FVW in 2010, which was 3.09 times the amount of agriculture and livestock. FVW was thrice as large as the total entity water, 4.07 times the amount of surface water, and 14.57 times the amount of underground water in Hunan Province. The distribution of FVW in Hunan Province is uneven and presents a trend that gradually increases from northeast to southwest; nevertheless, the trend is reasonable and in favour of alleviating and optimizing the pattern of water resources. Our analysis indicated that we need to improve the cognition of virtual water and pay due attention to FVW from the perspective of water management and allocation. Our results also indicated that forest and woody products are water intensive. An efficient method of balancing FVW and other uses of water is thus required; control and management of water consumption in forests should also be implemented under the condition of protecting the environment. For China, woody forest products should be mainly imported to improve water-use efficiency and relieve the shortage of water. Meanwhile, remote-sensing technology is a useful tool, and Chinese HJ-CCD images are an important data source for the estimation of FVW.
China’s large population and deteriorating environment have created great concern related to the sustainability of food production, especially since details related to this topic remain poorly studied. Thus, an integrated analysis of both crop yield and cultivated area is essential for gaining a better understanding of cereal grain production in China and for making corresponding policies designed to achieve food security. In this study, we adopt trend analysis of both provincial yield and cultivated area to assess the subsequent provincial-level cereal production sustainability between 1980 and 2011 with the goal of providing a better understanding of regional agricultural development. The results indicate that while maize shows the most promise for yield improvement, rice and wheat production is experiencing substantial yield stagnation among most provinces across mainland China. In addition, the trends in spatial patterns are prominently different from those of yields. The sizes of the main rice- and wheat-growing areas in China have declined greatly, suggesting that the related production of these cereals should attract more attention from land management planners and farmers. Specifically, the south-eastern coastal provinces have largely failed to sustain both crop yield and area, while the north-eastern provinces have witnessed thriving agricultural production during the last three decades. Moreover, we find that cereal production in China is significantly affected by governmental policies related to the agricultural sector. Thus, this analysis of food production in China will help policymakers to better understand how the potential implications of food security in China may be applicable to countries worldwide.
We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
- Apr 2016
Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982–2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated.
Extreme temperature impacts on field crop are of key concern and increasingly assessed, however the studies have seldom taken into account the automatic adaptations such as shifts in planting dates, phenological dynamics and cultivars. In this present study, trial data on rice phenology, agro-meteorological hazards and yields during 1981–2009 at 120 national agro-meteorological experiment stations were used. The detailed data provide us a unique opportunity to quantify extreme temperature impacts on rice yield more precisely and in a setting with automatic adaptations. In this study, changes in an accumulated thermal index (growing degree day, GDD), a high temperature stress index (>35 °C high temperature degree day, HDD), and a cold stress index (<20 °C cold degree day, CDD), were firstly investigated. Then, their impacts on rice yield were further quantified by a multivariable analysis. The results showed that in the past three decades, for early rice, late rice and single rice in western part, and single rice in other parts of the middle and lower reaches of Yangtze River, respectively, rice yield increased by 5.83%, 1.71%, 8.73% and 3.49% due to increase in GDD. Rice yield was generally more sensitive to high temperature stress than to cold temperature stress. It decreased by 0.14%, 0.32%, 0.34% and 0.14% due to increase in HDD, by contrast increased by 1.61%, 0.26%, 0.16% and 0.01% due to decrease in CDD, respectively. In addition, decreases in solar radiation reduced rice yield by 0.96%, 0.13%, 9.34% and 6.02%. In the past three decades, the positive impacts of increase in GDD and the negative impacts of decrease in solar radiation played dominant roles in determining overall climate impacts on yield. However, with climate warming in future, the positive impacts of increase in GDD and decrease in CDD will be offset by increase in HDD, resulting in overall negative climate impacts on yield. Our findings highlight the risk of heat stress on rice yield and the importance of developing integrated adaptation strategies to cope with heat stress.
- Jan 2016
Extensive studies had been conducted to investigate the impacts of climate change on maize growth and yield in recent decades; however, the dynamics of crop husbandry in response and adaptation to climate change were not taken into account. Based on field observations spanning from 1981 to 2009 at 167 agricultural meteorological stations across China, we found that solar radiation and temperature over the observed maize growth period had decreasing trends during 1981-2009, and maize yields were positively correlated with these climate variables in major production regions. The decreasing trends in solar radiation and temperature during maize growth period were mainly ascribed to the adoption of late maturity cultivars with longer reproductive growth period (RGP). The adoption of late maturing cultivars with longer RGP contributed substantially to grain yield increase during the last three decades. The climate trends during maize growth period varied among different production areas. During 1981-2009, decreases in mean temperature, precipitation and solar radiation over maize growth period jointly reduced yield most by 13.2-17.3% in southwestern China, by contrast in northwestern China increases in mean temperature, precipitation and solar radiation jointly increased yield most by 12.9-14.4%. Our findings highlight that the adaptations of maize production system to climate change through shifts of sowing date and genotypes are underway and should be taken into accounted when evaluating climate change impacts.
We used simple and explicit methods, as well as improved datasets for climate, crop phenology and yields, to address the association between variability in crop yields and climate anomalies in China from 1980 to 2008. We identified the most favourable and unfavourable climate conditions and the optimum temperatures for crop productivity in different regions of China. We found that the simultaneous occurrence of high temperatures, low precipitation and high solar radiation was unfavourable for wheat, maize and soybean productivity in large portions of northern, northwestern and northeastern China; this was because of droughts induced by warming or an increase in solar radiation. These climate anomalies could cause yield losses of up to 50 % for wheat, maize and soybeans in the arid and semi-arid regions of China. High precipitation and low solar radiation were unfavourable for crop productivity throughout southeastern China and could cause yield losses of approximately 20 % for rice and 50 % for wheat and maize. High temperatures were unfavourable for rice productivity in southwestern China because they induced heat stress, which could cause rice yield losses of approximately 20 %. In contrast, high temperatures and low precipitation were favourable for rice productivity in northeastern and eastern China. We found that the optimum temperatures for high yields were crop specific and had an explicit spatial pattern. These findings improve our understanding of the impacts of extreme climate events on agricultural production in different regions of China.
Using the detailed field experiment data from 1981 to 2009 at four representative agro-meteorological experiment stations in China, along with the Agricultural Production System Simulator (APSIM) rice-wheat model, we evaluated the impact of sowing/transplanting date on phenology and yield of rice-wheat rotation system (RWRS). We also disentangled the contributions of climate change, modern cultivars, sowing/transplanting density and fertilization management, as well as changes in each climate variables, to yield change in RWRS, in the past three decades. We found that change in sowing/transplanting date did not significantly affect rice and wheat yield in RWRS, although alleviated the negative impact of climate change to some extent. From 1981 to 2009, climate change jointly caused rice and wheat yield change by −17.4 to 1.5 %, of which increase in temperature reduced yield by 0.0–5.8 % and decrease in solar radiation reduced it by 1.5–8.7 %. Cultivars renewal, modern sowing/transplanting density and fertilization management contributed to yield change by 14.4–27.2, −4.7– −0.1 and 2.3–22.2 %, respectively. Our findings highlight that modern cultivars and agronomic management compensated the negative impacts of climate change and played key roles in yield increase in the past three decades.
The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.
The impact of climate change on crop yield is compounded by cultivar shifts and agronomic management practices. To determine the relative contributions of climate change, cultivar shift, and management practice to changes in maize (Zea mays L.) yield in the past three decades, detailed field data for 1981-2009 from four representative experimental stations in North China Plain (NCP) were analyzed via model simulation. The four representative experimental stations are geographically and climatologically different, represent the typical cropping system in the study area, and have more complete weather/crop records for the period of 1981-2009. The results showed that while the shift from traditional to modern cultivar increased yield by 23.9-40.3 %, new fertilizer management increased yield by 3.3-8.6 %. However, the trends in climate variables for 1981-2009 reduced maize yield by 15-30 % in the study area. Among the main climate variables, solar radiation had the largest effect on maize yield, followed by temperature and then precipitation. While a significant decline in solar radiation in 1981-2009 (maybe due to air pollution) reduced yield by 12-24 %, a significant increase in temperature reduced yield by 3-9 %. In contrast, a non-significant increase in precipitation during the maize growth period increased yield by 0.9-3 % at three of the four investigated stations. However, a decline in precipitation reduced yield by 3 % in the remaining station. The study revealed that although the shift from traditional to modern cultivars and agronomic management practices contributed most to the increase in maize yield, the negative impact of climate change was large enough to offset 46-67 % of the trend in the observed yields in the past three decades in NCP. The reduction in solar radiation, especially in the most critical period of maize growth, limited the process of photosynthesis and thereby further reduced maize yield.
Extreme temperature stress (ETS) is recognized as an important threat to the food supply in China. However, how much yield loss caused by ETS (YLETS) to the irrigated rice production still remains unclear. In this study, we provided a prototype for YLETS assessments by using a process-based crop model (MCWLA-Rice) with the ETS impacts explicitly parameterized, to help understand the spatio-temporal patterns of YLETS and the mechanism underlying the ETS impacts at a 0.5° × 0.5° grid scale in the major irrigated rice planting areas across China during 1981–2010. On the basis of the optimal 30 sets of parameters, the ensemble simulations indicated the following: Regions I (northeastern China) and III2 (the mid-lower reaches of the Yangtze River) were considered to be the most vulnerable areas to ETS, with the medium YLETS of 18.4 and 12.9 %, respectively. Furthermore, large YLETS values (>10 %) were found in some portions of Region II (the Yunnan-Guizhou Plateau), western Region III1 (the Sichuan Basin), the middle of Region IV_ER (southern China cultivated by early rice), and the west and southeast of Region IV_LR (southern China cultivated by late rice). Over the past several decades, a significant decrease in YLETS was detected in most of Region I and in northern Region IV_LR (with the medians of −0.53 and −0.28 % year−1, respectively). However, a significant increase was found in most of Region III (including III1 and III2) and in Region IV_ER, particularly in the last decade (2001–2010). Overall, reduced cold stress has improved the conditions for irrigated rice production across large parts of China. Nevertheless, to improve the accuracy of YLETS estimations, more accurate yield loss functions and multimodel ensembles should be developed.
Heat stress impacts on crop growth and yield have been investigated by controlled-environment experiments , however little is known about the impacts under field conditions at large spatial and temporal scales, particularly in a setting with farmers' autonomous adaptations. Here, using detailed experiment observations at 34 national agricultural meteorological stations spanning from 1981 to 2009 in the Huang-Huai-Hai Plain (HHHP) of China, we investigated the changes in climate and heat stress during wheat reproductive growing period (from heading to maturity) and the impacts of climate change and heat stress on reproductive growing duration (RGD) and yield in a setting with farmers' autonomous adaptations. We found that RGD and growing degree days above 0 • C (GDD) from heading to maturity increased, which increased yield by ∼14.85%, although heat stress had negative impacts on RGD and yield. During 1981–2009, high temperature (>34 • C) degree days (HDD) increased in the northern part, however decreased in the middle and southern parts of HHHP due to advances in heading and maturity dates. Change in HDD, together with increase in GDD and decrease in solar radiation (SRD), jointly increased wheat yield in the northern and middle parts but reduced it in the southern part of HHHP. During the study period, increase in GDD and decrease in SRD had larger impacts on yield than change in HDD. However, with climate warming of 2 • C, damage of heat stress on yield may offset a large portion of the benefits from increases in RGD and GDD, and eventually result in net negative impacts on yield in the northern part of HHHP. Our study showed that shifts in cultivars and wheat production system dynamics in the past three decades reduced heat stress impacts in the HHHP. The insights into crop response and adaptation to climate change and climate extremes provide excellent evidences and basis for improving climate change impact study and designing adaptation measures for the future.
Winter wheat production in northern China severely suffered from high temperatures and low relative humidity. However, the spatio-temporal pattern of heat stress and dry stress and the impacts of these multi-hazards on winter wheat yield have rarely been investigated. Using historical climate data, phenology data and yield records from 1980 to 2008, an analysis was performed to characterize the spatio-temporal variability of heat stress and dry stress in the post-heading stages of wheat growth in northern China. Additionally, these stresses' impacts on winter wheat yield fluctuations were evaluated. Spatially, the central and northern parts of northern China have seen more serious heat stress, while greater dry stress has been observed in the northwest and north of the research area. Temporally, the heat stress has increased in the western part but decreased in the central and eastern parts of research area. Dry stress has aggravated in the entire northern China during the past decades, indicating the complexity of the exposure to adverse climate conditions. These two hazards (heat stress and dry stress) have contributed significant yield loss (up to 1.28% yieldyr-1) in most parts of the research region. The yield in the west was more sensitive to heat stress, and dry stress was the main hazard in the south. Additionally, the opposite spatial pattern between the sensitivity and exposure revealed that the climate is not the only factor controlling the yield fluctuation, the local adaptation measures used to mitigate negative influences of extreme events should not be ignored. In general, this study highlighted a focus on the impacts of multi-hazards on agricultural production, and an equal importance of considering local adaptation ability during the evaluation of agricultural risk in the future. Additionally, paying more attention to higher sensitive areas and to more reasonable and practical adaptive strategies is critical and significant for food supply security.
The long-term field experiment data at four representative agro-meteorological stations, together with a crop simulation model, were used to disentangle the contributions of climate change, variety renewal, and fertilization management to rice yield change in the past three decades. We found that during 1981–2009 varieties renewal increased rice yield by 16%–52%, management improvement increased yield by 0–16%, and the contributions of climate change to rice yield varied from — 16% to 10%. Varieties renewal and management improvement offset the negative impacts of climate change on rice production. Among the major climate variables, decreases in solar radiation reduced rice yield on average by 0.1%per year. The impact of temperature change had an explicit spatial pattern. It increased yield by 0.04%–0.4% per year for single rice at Xinbin and Ganyu station and for late rice at Tongcheng station, by contrast reduced yield by 0.2%–0.4% per year for single rice at Mianyang station and early rice at Tongcheng station. During 1981–2009, rice varieties renewal was characterized by increases in thermal requirements, grain number per spike and harvest index. The new varieties were less sensitive to climate change than old ones. The development of high thermal requirements, high yield potential and heat tolerant rice varieties, together with improvement of agronomic management, should be encouraged to meet the challenges of climate change and increasing food demand in future. © 2015 Higher Education Press and Springer-Verlag Berlin Heidelberg
- Oct 2015
The precise spatially explicit knowledge about crop yield potentials and yield gaps is essential to guide sustainable intensification of agriculture. In this study, the maize yield potentials from 1980 to 2008 across the major maize production regions of China were firstly estimated by county using ensemble simulation of a well-validated large scale crop model, i.e., MCWLA-Maize model. Then, the temporal and spatial patterns of maize yield potentials and yield gaps during 1980–2008 were presented and analyzed. The results showed that maize yields became stagnated at 32.4% of maize-growing areas during the period. In the major maize production regions, i.e., northeastern China, the North China Plain (NCP) and southwestern China, yield gap percentages were generally less than 40% and particularly less than 20% in some areas. By contrast, in northern and southern China, where actual yields were relatively lower, yield gap percentages were generally larger than 40%. The areas with yield gap percentages less than 20% and less than 40% accounted for 8.2% and 27.6% of maize-growing areas, respectively. During the period, yield potentials decreased in the NCP and southwestern China due to increase in temperature and decrease in solar radiation; by contrast, increased in northern, northeastern and southeastern China due to increases in both temperature and solar radiation. Yield gap percentages decreased generally by ∼2% per year across the major maize production regions, although increased in some areas in northern and northeastern China. The shrinking of yield gap was due to increases in actual yields and decreases in yield potentials in the NCP and southwestern China; and due to larger increases in actual yields than in yield potentials in northeastern and southeastern China. The results highlight the importance of sustainable intensification of agriculture to close yield gaps, as well as breeding new cultivars to increase yield potentials, to meet the increasing food demand.
This paper aims to assess the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30-year continuous cropping systems for two crops (winter wheat and silage maize) under three production conditions for the state of North Rhine-Westphalia, Germany. DAE was evaluated for five weather data resolutions (i.e. 1, 10, 25, 50 and 100 km) for three response variables including yield, growing season evapotranspiration (ET) and water use efficiency (WUE). Five metrics, the spatial difference (∆), average absolute deviation (AAD), relative AAD (rAAD), root mean squared error (RMSE), and relative root mean squared error (rRMSE) were used to evaluate the DAE on both the input weather data and simulated results. We found, for weather data, that the data aggregation narrowed the spatial variability but widened the spatial difference (∆), especially across the mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but only slightly changed for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
ABSTRACT: Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize ) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha −1 , whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.
This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (−2 to +9°C) and precipitation (−50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.
We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias ( Δ ), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the Δ , especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
Concerns have been raised among policy-makers, researchers and Chinese citizens regarding the widespread environmental degradation that has occurred in China in recent decades. Years of environmental education and media coverage on pollution harm and health risks have not only provided information about pollution to the public, but have also strengthened people’s concerns. However, an ‘intense focus’ on pollution is far from sufficient; at present, it is necessary to assess to what extent the public can identify specific environmental conditions and whether they are ready to cope with potential health risks from pollution. Through face-to-face surveys on trains and at railway stations nationwide, we investigated public experiences of environmental pollution accidents, perceptions of local environmental risks (focused on air and water quality) and responses to local environmental conditions. By comparing public perceptions with official environmental monitoring data-sets, we concluded that the accuracy of perceptions related to four environmental factors ranged from 40 to 60% at the individual scale. Furthermore, the accuracies increased approximately 2–10% at the county scale and 10–30% at the city scale, highlighting the possible benefits of collective intelligence in helping the public to identify existing environment conditions more accurately. Additionally, despite great concerns about pollution and health, public attitudes toward coping with the dangers of pollution and health risks were found to be indifferent. Our study revealed factors at the individual, social and governmental levels that led to low levels of perception accuracy and response scores. Thereout, we stressed potential pathways to improve the accuracy of public perception and the positivity of responses. The survey results indicate that there is a long way to go before the public is well prepared to cope with the risks of pollution; as a consequence, it is necessary to improve both personal environmental awareness as well as governmental, social and commercial responses to pollution events.
Worsening water storage depletion (WSD) contributes to environmental degradation, land subsidence and earthquake and could disrupt food production/security and social stability. There is need for efficient water use strategies in North China, a pivotal agrarian, industrial and political base in China with a widespread WSD. This study integrates satellite, model and field data products to investigate WSD and land subsidence in North China. In the first step, GRACE (Gravity Recovery and Climate Experiment) mass rates are used to show WSD in the region. Next, GRACE total water storage (TWS) is corrected for soil water storage (SWS) to derive groundwater storage (GWS) using GLDAS (Global Land Data Assimilation System) data products. The derived GWS is compared with GWS obtained from field-measured groundwater level to show land subsidence in the study area. Then GPS (Global Positioning System) data of relative land surface change (LSC) are used to confirm the subsidence due to WSD. A total of ~ 96 near-consecutive months (January 2002 through December 2009) of datasets are used in the study. Based on GRACE mass rates, TWS depletion is 23.76 ± 1.74 mm yr−1 or 13.73 ± 1.01 km3 yr−1 in the 578 000 km2 study area. This is ~ 31 % of the slated 45 km3 yr−1 water delivery in 2050 via the South–North Water Diversion Project. Analysis of relative LSC shows subsidence of 7.29 ± 0.35 mm yr−1 in Beijing and 2.74 ± 0.16 mm yr−1 in North China. About 11.53 % (2.74 ± 0.18 mm or 1.58 ± 0.12 km3) of the TWS and 8.37 % (1.52 ± 0.70 mm or 0.88 ± 0.03 km3) of the GWS are attributed to storage reductions accompanying subsidence in the region. Although interpretations of the findings require caution due to the short temporal and large spatial coverage, the concurrence of WSD and land subsidence could have adverse implications for the study area. It is critical that the relevant stakeholders embark on resource-efficient measures to ensure water availability, food security, ecological sustainability and social stability in this pivotal region.
- May 2015
The El Niño Southern Oscillation (ENSO) is one of the main factors influencing global climate variability and consequently has a major effect on crop yield variability. However, most studies have been based on statistical approaches, which make it difficult to discover the underlying impact mechanisms. Here, using process-based crop model Model to Capture the Crop-Weather relationship over a Large Area (MCWLA)-Maize, we found a consistent spatial pattern of maize yield variability in association with ENSO between MCWLA-Maize model outputs and observations. During El Niño years, most areas of China, especially in the north, experience a yield increase, whereas some areas in the south have a decrease in yields. During La Niña years, there is an obvious decline in yields, mainly in the north and northeast, and a general increase in the south. In-depth analyses suggest that precipitation P rather than temperature T and solar radiation S during the maize growing season is the main cause of ENSO-induced maize yield variability in northern and northeastern China. Although a 2 °C change of T can affect maize yields more than a 20% change of P, greater changes of P contribute more to maize yield variability during ENSO years. In general, maize yields in drier regions are much more sensitive to P variability than those in wetter areas. All changes in meteorological variables, including T, P, S, and vapour pressure deficit (VPD) during ENSO years, affect yield variability mainly through their effects on water stress. Our results suggest that more effective agricultural information can be provided to government decision makers and farmers by developing a food security warning system based on the MCWLA-Maize model and ENSO forecast information.
As climate change could significantly influence crop phenology and subsequent crop yield, adaptation is a critical mitigation process of the vulnerability of crop growth and production to climate change. Thus, to ensure crop production and food security, there is the need for research on the natural (shifts in crop growth periods) and artificial (shifts in crop cultivars) modes of crop adaptation to climate change. In this study, field observations in 18 stations in North China Plain (NCP) are used in combination with Agricultural Production Systems Simulator (APSIM)-Maize model to analyze the trends in summer maize phenology in relation to climate change and cultivar shift in 1981-2008. Apparent warming in most of the investigated stations causes early flowering and maturity and consequently shortens reproductive growth stage. However, APSIM-Maize model run for four representative stations suggests that cultivar shift delays maturity and thereby prolongs reproductive growth (flowering to maturity) stage by 2.4−3.7 day per decade (d 10a−1). The study suggests a gradual adaptation of maize production process to ongoing climate change in NCP via shifts in high thermal cultivars and phenological processes. It is concluded that cultivation of maize cultivars with longer growth periods and higher thermal requirements could mitigate the negative effects of warming climate on crop production and food security in the NCP study area and beyond.
FACCE MACSUR Reports 2, 5, SP5-46
- Mar 2015
A major challenge of the 21(st) century is to achieve food supply security under a changing climate and roughly a doubling in food demand by 2050 compared to present, the majority of which needs to be met by the cereals wheat, rice, maize, and barley. Future harvests are expected to be especially threatened through increased frequency and severity of extreme events, such as heat waves and drought, that pose particular challenges to plant breeders and crop scientists. Process-based crop models developed for simulating interactions between genotype, environment, and management are widely applied to assess impacts of environmental change on crop yield potentials, phenology, water use, etc. During the last decades, crop simulation has become important for supporting plant breeding, in particular in designing ideotypes, i.e. 'model plants', for different crops and cultivation environments. In this review we (i) examine the main limitations of crop simulation modelling for supporting ideotype breeding, (ii) describe developments in cultivar traits in response to climate variations, and (iii) present examples of how crop simulation has supported evaluation and design of cereal cultivars for future conditions. An early success story for rice demonstrates the potential of crop simulation modelling for ideotype breeding. Combining conventional crop simulation with new breeding methods and genetic modelling holds promise to accelerate delivery of future cereal cultivars for different environments. Robustness of model-aided ideotype design can further be enhanced through continued improvements of simulation models to better capture effects of extremes and the use of multi-model ensembles. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: email@example.com.
Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared.
Increasing demand for food, driven by unprecedented population growth and increasing consumption, will keep challenging food security in China. Although cereal yields have substantially improved during the last three decades, whether it will keep thriving to meet the increasing demand is not known yet. Thus, an integrated analysis on the trends of crop yield and cultivated area is essential to better understand current state of food security in China, especially on county scale. So far, yield stagnation has extensively dominated the main cereal-growing areas across China. Rice yield is facing the most severe stagnation that 53.9% counties tracked in the study have stagnated significantly, followed by maize (42.4%) and wheat (41.9%). As another important element for production sustainability, but often neglected is the planted area patterns. It has been further demonstrated that the loss in productive arable land for rice and wheat have dramatically increased the pressure on achieving food security. Not only a great deal of the planted areas have stagnated since 1980, but also collapsed. 48.4% and 54.4% of rice- and wheat-growing counties have lost their cropland areas to varying degrees. Besides, 27.6% and 35.8% of them have retrograded below the level of the 1980s. The combined influence (both loss in yield and area) has determined the crop sustainable production in China to be pessimistic for rice and wheat, and consequently no surprise to find that more than half of counties rank a lower level of production sustainability. Therefore, given the potential yield increase in wheat and maize, as well as substantial area loss of rice and wheat, the possible targeted adaptation measures for both yield and cropping area is required at county scale. Moreover, policies on food trade, alongside advocation of low calorie diets, reducing food loss and waste can help to enhance food security.
Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.
- Jan 2015
The effect of variation in seasonal temperature and precipitation on soil water nitrate (NO3N) concentration and leaching from winter and spring cereals cropping systems was investigated over three consecutive four-year crop rotation cycles from 1997 to 2008 in an organic farming crop rotation experiment in Denmark. Three experimental sites, varying in climate and soil type from coarse sand to sandy loam, were investigated. The experiment included experimental treatments with different rotations, manure rate and cover crop, and soil nitrate concentrations was monitored using suction cups. The effects of climate, soil and management were examined in a linear mixed model, and only parameters with significant effect (P < 0.05) were included in the final model. The model explained 61% and 47% of the variation in the square root transform of flow-weighted annual NO3N concentration for winter and spring cereals, respectively, and 68% and 77% of the variation in the square root transform of annual NO3N leaching for winter and spring cereals, respectively. Nitrate concentration and leaching were shown to be site specific and driven by climatic factors and crop management. There were significant effects on annual N concentration and NO3N leaching of location, rotation, previous crop and crop cover during autumn and winter. The relative effects of temperature and precipitation differed between seasons and cropping systems. A sensitivity analysis revealed that the predicted N concentration and leaching increased with increases in temperature and precipitation.
Our understanding on the impact of climate change on agricultural production, as well as the potential adaption options, can be accelerated by shedding insights on the historical experiences in the past few decades. Here, we used improved datasets of climate, crop phenology, and crop yields to investigate climate–crop yield relationships, recent trends in seasonal climate and their impact on yields of major crops (i.e., rice, wheat, maize, and soybean) by county throughout China during the period of 1980–2008. The temporal and spatial patterns of climate trends and the impact on major crop yields were presented. We found crop yields declined by up to 5–10 % or more for each 1 °C increase in mean temperature over crop growing period at some regions, and trends in mean temperature during the period of 1980–2008 reduced crop yields by up to 2.5–5.0 % or more at some regions. For the whole country, planting area-weighted average of yield change due to trends in mean temperature and precipitation together was about 1.16, −0.31, −0.40, and 0.11 % over the whole period for rice, wheat, maize, and soybean, respectively. Climate trends were large enough at some regions to offset a notable portion of the increases in average yields that arose from technology and other factors. The particular crops and regions that have been most affected and should be the priorities to adapt are maize and wheat in the arid and semi-arid areas of northern and northeastern China, where climate warming-induced droughts are one of major challenges.