scholarly journals Climate-Induced Yield Losses for Winter Wheat in Henan Province, North China and Their Relationship with Circulation Anomalies

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3341
Author(s):  
Hui Zheng ◽  
Jin Huang ◽  
Jiadong Chen
Keyword(s):  
Winter Wheat ◽  
Large Scale ◽  
North China ◽  
Southern Oscillation ◽  
Assessment Method ◽  
Henan Province ◽  
Western Hemisphere ◽  
Yield Losses ◽  
Yield Data ◽  
Atmospheric Circulation Indices

Risk analysis using climate-induced yield losses (CIYL) extracted from long-term yield data have been recognized in China, but the research focusing on the time-series characteristics of risk and the circulation signals behind yield losses still remains incomplete. To address these challenges, a case study on winter wheat production in Henan province, north China was conducted by using annual series of yield in 17 cities during 1988–2017 and monthly series of 15 types of large-scale oceanic-atmospheric circulation indices (LOACI). A comprehensive risk assessment method was established by combining the intensity, frequency, and variability of CIYL and principal component analysis (PCA). The results showed that the westernmost Henan was identified as the area of higher-risk. PCA and Mann–Kendall trend tests indicated that the southern, northern, eastern, and western areas in Henan province were classified as having different annual CIYL variations in these four sub-regions; the decreasing trend of CIYL in northern area was the most notable. Since the 2000s, a significant decline in CIYL was found in each sub-region. It should be noted that the key LOACI, which includes Tropical Northern Atlantic Index (TNA), Western Hemisphere warm pool (WHWP), and Southern oscillation index (SOI), indicated significant CIYL anomalies in some months. Furthermore, the regional yield simulation results using linear regression for the independent variables of year and various LOACI were satisfactory, with the average relative error ranging from 3.48% to 6.87%.

scholarly journals Impact of extreme weather conditions on European crop production in 2018

2020 ◽  
Vol 375 (1810) ◽  
pp. 20190510 ◽  
Author(s):  
Damien Beillouin ◽  
Bernhard Schauberger ◽  
Ana Bastos ◽  
Phillipe Ciais ◽  
David Makowski
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Large Scale ◽  
Extreme Weather ◽  
Severe Drought ◽  
Yield Losses ◽  
Learning Models ◽  
Yield Data ◽  
Continental Scale ◽  
Machine Learning Models

Extreme weather increases the risk of large-scale crop failure. The mechanisms involved are complex and intertwined, hence undermining the identification of simple adaptation levers to help improve the resilience of agricultural production. Based on more than 82 000 yield data reported at the regional level in 17 European countries, we assess how climate affected the yields of nine crop species. Using machine learning models, we analyzed historical yield data since 1901 and then focus on 2018, which has experienced a multiplicity and a diversity of atypical extreme climatic conditions. Machine learning models explain up to 65% of historical yield anomalies. We find that both extremes in temperature and precipitation are associated with negative yield anomalies, but with varying impacts in different parts of Europe. In 2018, Northern and Eastern Europe experienced multiple and simultaneous crop failures—among the highest observed in recent decades. These yield losses were associated with extremely low rainfalls in combination with high temperatures between March and August 2018. However, the higher than usual yields recorded in Southern Europe—caused by favourable spring rainfall conditions—nearly offset the large decrease in Northern European crop production. Our results outline the importance of considering single and compound climate extremes to analyse the causes of yield losses in Europe. We found no clear upward or downward trend in the frequency of extreme yield losses for any of the considered crops between 1990 and 2018. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

A validation of eddy covariance technique for measuring crop evapotranspiration on different time scales in the North China Plain

2020 ◽  
pp. 1-13
Author(s):  
Fangfang Wang ◽  
Donghao Ma ◽  
Wenju Zhao ◽  
Yunxuan Lu ◽  
Ding Zhou ◽  
...  
Keyword(s):  
Energy Balance ◽  
Winter Wheat ◽  
Time Scale ◽  
Large Scale ◽  
North China Plain ◽  
North China ◽  
Summer Maize ◽  
The North ◽  
The North China Plain ◽  
Different Time Scales

Accurate determination of evapotranspiration (ET) has tremendous potential in guiding irrigation and improving the efficiency of water resources utilization in the North China Plain. Eddy covariance (EC) method is currently a popular method for determining field-scale ET. However, due to varying foot print and unclosed energy balance, the applicability of EC in different regions needs to be tested and corrected. In present work, we compared the ET of the winter wheat – summer maize rotation cropland measured by the EC method with the ET measured by large-scale lysimeters on different time scales. The degree of energy balance closure of EC measurements in this region is 78%. After adjusted by using Bowen ratio forced closure method, the ET monitored by EC is comparable with those monitored by large-scale lysimeters. The results also indicated that the consistency of the observed ET by the EC and lysimeters got better with an increasing time scale, especially for the multi-year average ET values with a relative deviation of less than 1%. The short-time events such as irrigation and precipitation and the mismatch of the varying footprint area of the EC and the small fixed source area of the lysimeter should be responsible for the discrepancy of ET in two methods on daily scale. However, the factors of crop biomass, total available water, and local climate condition exert more effects on the observed ET on large time scale. Overall, the EC technique is responsible for ET measurement of winter wheat – summer maize rotation cropland of the North China Plain.

scholarly journals Influence of Climate Variability on Soybean Yield in MATOPIBA, Brazil

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1130
Author(s):  
Layara Reis ◽  
Cláudio Moisés Santos e Silva ◽  
Bergson Bezerra ◽  
Pedro Mutti ◽  
Maria Helena Spyrides ◽  
...  
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Meteorological Data ◽  
Tropical Atlantic ◽  
Climate Scenarios ◽  
Yield Losses ◽  
Agricultural Risk ◽  
Area Of Interest ◽  
Sowing Dates ◽  
Culture Model

The objective of this study was to analyze the influence of large-scale atmospheric–oceanic mechanisms (El Niño–Southern Oscillation—ENSO and the inter-hemispheric thermal gradient of the Tropical Atlantic) on the spatial–temporal variability of soy yield in MATOPIBA. The following, available in the literature, were used: (i) daily meteorological data from 1980 to 2013 (Xavier et al., 2016); (ii) (chemical, physical, and hydric) properties of the predominant soil class in the area of interest, available at the World Inventory of Soil Emission Potentials platform; (iii) genetic coefficients of soybean cultivar with Relative Maturity Group adapted to the conditions of the region. The simulations were performed using the CROPGRO-Soybean culture model of the Decision Support System for Agrotechnology Transfer (DSSAT) system, considering sowing dates between the months of October and December of 33 agricultural years, as well as for three meteorological scenarios (climatology, favorable-wet, and unfavorable-dry). Results showed that the different climate scenarios can alter the spatial patterns of agricultural risk. In the favorable-wet scenario, there was a greater probability of an increase in yield and a greater favorable window for sowing soybean, while in the unfavorable-dry scenario these values were lower. However, considering the unfavorable-dry scenario, in some areas the reduction in yield losses will depend on the chosen planting date.

Long-Range Predictability in the Tropics. Part I: Monthly Averages

2005 ◽  
Vol 18 (5) ◽  
pp. 619-633 ◽  
Author(s):  
Thomas Reichler ◽  
John O. Roads
Keyword(s):  
Boundary Conditions ◽  
Long Range ◽  
Large Scale ◽  
Initial Conditions ◽  
Southern Oscillation ◽  
Western Hemisphere ◽  
Eastern Hemisphere ◽  
Condition Effect ◽  
Temporal And Spatial Characteristics ◽  
Hemisphere Winter

Abstract The sensitivity to initial and boundary conditions of monthly mean tropical long-range forecasts (1–14 weeks) during Northern Hemisphere winter is studied with a numerical model. Five predictability experiments with different combinations of initial conditions and prescribed ocean boundary conditions are conducted in order to investigate the temporal and spatial characteristics of the perfect model forecast skill. It is shown that initial conditions dominate a tropical forecast during the first 3 weeks and that they influence a forecast for at least 8 weeks. The initial condition effect is strongest over the Eastern Hemisphere and during years when the El Niño–Southern Oscillation (ENSO) phenomenon is weak. The relatively long sensitivity to initial conditions is related to a complex combination of dynamic and thermodynamic effects, and to positive internal feedbacks of large-scale convective anomalies. At lead times of more than 3 weeks, boundary forcing is the main contributor to tropical predictability. This effect is particularly strong over the Western Hemisphere and during ENSO. Using persisted instead of observed sea surface temperatures leads to useful forecast results only over the Western Hemisphere and during ENSO.

scholarly journals Rice Yield Responses in Bangladesh to Large-scale Atmospheric Oscillation Using Multifactorial Model

2021 ◽  
Author(s):  
Bonosri Ghose ◽  
Abu Reza Md. Towfiqul I ◽  
Roquia Salam ◽  
Shamsuddin Shahid ◽  
Md. Kamruzzaman ◽  
...  
Keyword(s):  
Large Scale ◽  
Rice Yield ◽  
Principal Component ◽  
Northern Region ◽  
Wavelet Coherence ◽  
Early Assessment ◽  
Yield Data ◽  
Atmospheric Circulation Indices ◽  
Northwestern Region ◽  
Yield Responses

Abstract This paper intends to explore rice yield fluctuations to large-scale atmospheric circulation indices (LACIs) in Bangladesh. The annual dataset of climate-derived yield index (CDYI), estimated using principal component analysis of Aus rice yield data of 23 districts, and five LACIs for the period 1980-2017 were used for this purpose. The key outcomes of the study were as follows: (1) three sub-regions of Bangladesh, northern, northwestern, and northeastern, showed different kinds of CDYI anomalies; (2) the CDYI time series in northern and northeastern regions exhibited a substantial 6-year fluctuation, whereas a 2.75 to 3-year fluctuation predominated the northwestern region; (3) rice yield showed the highest sensitivity of LACIs in the northern region; (4) Indian Ocean dipole (IOD) and East Central Tropical Pacific SST (Nino 3.4) in July, and IOD index in March provide the best yield forecasting signals for northern, northwestern, and northeastern regions, respectively; (5) wavelet coherence study demonstrated noteworthy in-phase and out-phases coherences between key climatic variables (KCVs) and CDYI anomalies at various time-frequencies in three sub-regions; (6) the random forest (RF) model revealed the IOD as the vital contributing factor of rice yield fluctuations in the country; (6) the multi-factorial model with different LACIs and year as predictors can predict rice yield, with the mean relative error (MRE) in the range of 4.82 to 5.51% only. The generated knowledge can be used for an early assessment of rice yield and recommend policy directives to ensure food security.

scholarly journals Long-Term Variability of Relationships between Potential Large-Scale Drivers and Summer Precipitation in North China in the CERA-20C Reanalysis

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 81
Author(s):  
Lan Dai ◽  
Jonathon S. Wright
Keyword(s):  
Twentieth Century ◽  
Large Scale ◽  
North China ◽  
Southern Oscillation ◽  
Summer Precipitation ◽  
Strong Relationship ◽  
Atmospheric Model ◽  
Energy Budgets ◽  
Climate Projections ◽  
Seasonal Forecasts

Although much progress has been made in identifying the large-scale drivers of recent summer precipitation variability in North China, the evolution of these drivers over longer time scales remains unclear. We investigate multidecadal and interannual variability in North China summer precipitation in the 110-year Coupled ECMWF Reanalysis of the Twentieth Century (CERA-20C), considering changes in regional moisture and surface energy budgets along with nine circulation indices linked to anomalous precipitation in this region. The CERA-20C record is separated into three distinct periods according to the running climatology of summer precipitation: 1901–1944 (neutral), 1945–1979 (wet), and 1980–2010 (dry). CERA-20C reproduces expected relationships between large-scale drivers and regional summer precipitation anomalies well during 1980–2010, but these relationships generally do not extend to earlier periods. For example, a strong relationship with the Eurasian teleconnection pattern only emerges in the late 1970s, while correlations with the El Niño-Southern Oscillation and the Pacific–Japan pattern change sign in the mid-twentieth century. We evaluate two possible reasons for this nonstationarity: (1) the underlying atmospheric model may require strong data assimilation constraints to capture large-scale circulation influences on North China, or (2) large-scale drivers inferred from recent records may be less general than expected. Our analysis indicates that both factors contribute to the identified nonstationarity in CERA-20C, with implications for the reliability of seasonal forecasts and climate projections based on current models.

scholarly journals A Satellite-Based Method for National Winter Wheat Yield Estimating in China

2021 ◽  
Vol 13 (22) ◽  
pp. 4680
Author(s):  
Yangyang Fu ◽  
Jianxi Huang ◽  
Yanjun Shen ◽  
Shaomin Liu ◽  
Yong Huang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Harvest Index ◽  
Crop Production ◽  
Large Scale ◽  
Gross Primary Production ◽  
Wheat Yield ◽  
Model Performance ◽  
Production Model ◽  
Yield Data ◽  
The Mean

Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale.

scholarly journals Decisive Atmospheric Circulation Indices for July–August Precipitation in North China Based on Tree Models

2019 ◽  
Vol 20 (8) ◽  
pp. 1707-1720 ◽  
Author(s):  
Xuan Tong ◽  
Zhongwei Yan ◽  
Jiangjiang Xia ◽  
Xiao Lou
Keyword(s):  
Large Scale ◽  
North China ◽  
Eastern China ◽  
Subtropical High ◽  
Northwestern Pacific ◽  
North African ◽  
Climate Services ◽  
Atmospheric Circulation Indices ◽  
Circulation Indices ◽  
High Ridge

Abstract Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns.

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