Agrometeorological Disaster Research Articles

Monitoring 2019 Drought and Assessing Its Effects on Vegetation Using Solar-Induced Chlorophyll Fluorescence and Vegetation Indexes in the Middle and Lower Reaches of Yangtze River, China

Monitoring drought precisely and evaluating drought effects quantitatively can establish a scientific foundation for understanding drought. Although solar-induced chlorophyll fluorescence (SIF) can detect the drought stress in advance, the performance of SIF in monitoring drought and assessing drought-induced gross primary productivity (GPP) losses from lush to senescence remains to be further studied. Taking the 2019 drought in the middle and lower reaches of the Yangtze River (MLRYR) as an example, this study aims to monitor and assess this drought by employing a new global, OCO-2-based SIF (GOSIF) and vegetation indexes (VIs). Results showed that the GPP, GOSIF, and VIs all exhibited significant increasing trends during 2000–2020. GOSIF was most consistent with GPP in spatial distribution and was most correlated with GPP in both annual (linear correlation, R2 = 0.87) and monthly (polynomial correlation, R2 = 0.976) time scales by comparing with VIs. During July–December 2019, the precipitation (PPT), soil moisture, and standardized precipitation evapotranspiration index (SPEI) were generally below the averages during 2011–2020 and reached their lowest point in November, while those of air temperature (Tem), land surface temperature (LST), and photosynthetically active radiation (PAR) were the contrary. For drought monitoring, the spatial distributions of standardized anomalies of GOSIF and VIs were consistent during August–October 2019. In November and December, however, considering vegetation has entered the senescence stage, SIF had an obvious early response in vegetation physiological state monitoring compared with VIs, while VIs can better indicate meteorological drought conditions than SIF. For drought assessment, the spatial distribution characteristics of GOSIF and its standardized anomaly were both most consistent with that of GPP, especially the standardized anomaly in November and December. All the above phenomena verified the good spatial consistency between SIF and GPP and the superior ability of SIF in capturing and quantifying drought-induced GPP losses. Results of this study will improve the understanding of the prevention and reduction in agrometeorological disasters and can provide an accurate and timely method for drought monitoring.

Assessment of Maize Drought Risk in Midwestern Jilin Province: A Comparative Analysis of TOPSIS and VIKOR Models

Drought is a major natural disaster that causes a reduction in rain-fed maize yield. Agricultural drought risk assessment is conducive to improving regional disaster management ability, thereby reducing food security risks and economic losses. Considering the complexity of risk assessment research, an increasing number of researchers are focusing on the multiple-criteria decision-making (MCDM) method. However, the applicability of the MCDM method to agro-meteorological disaster risk assessments is not clear. Therefore, this study comprehensively evaluated hazard, exposure, vulnerability, and emergency response and recovery capability using the TOPSIS and VIKOR models to generate a maize drought risk map in mid-western Jilin Province and ranked the drought risk of each county. The results showed that: (1) maize drought risk in the middle and west of Jilin province showed an increasing trend. Spatially, the risk diminished from west to east. The drought risks faced by Tongyu, Changchun, and Dehui were more severe; (2) the evaluation results of the two models were verified using the yield reduction rate. The VIKOR model was found to be more suitable for agrometeorological drought risk assessments; (3) according to the damage degree of drought disaster to maize, the cluster analysis method was used to divide the study area into three sub-regions: safe, moderate drought, and severe drought. Combined with the characteristics of different regions, suggestions on disaster prevention and mitigation are proposed. The results of this study can provide a basis for formulating strategies to alleviate drought, reduce losses, and ensure the sustainable development of agriculture.

Optimizing Center Pivot Irrigation to Regulate Field Microclimate and Wheat Physiology under Dry-Hot Wind Conditions in the North China Plain

The dry-hot wind climate is one of the major agro-meteorological disasters associated with high temperature, low humidity, and specific wind forces, which seriously affects the yield of wheat in the North China Plain. A field experiment was conducted to investigate the field microclimate, net photosynthetic rate, chlorophyll content of flag leaves, grain filling rate, and wheat yield after sprinkler misting under the condition of a dry-hot wind climate in the 2018 and 2019 seasons. Two travel speeds, full and half speed, and the corresponding irrigation amounts of 2.5 and 5 mm were used by a center pivot irrigation system during dry-hot wind conditions. A treatment without irrigation was applied as a control. The results showed that the air temperature and relative air humidity were greatly improved within 60 min after irrigation, especially in the upper part of the canopy. The net photosynthetic rate of flag leaves under 5 mm irrigation was higher than that under 2.5 mm irrigation during the middle and late grain filling periods. The adverse effects of dry-hot wind on the chlorophyll content of the flag leaves were mainly concentrated in the late grain filling stage. In the two years of the experiment, the average 1000-grain weights of 5 and 2.5 mm of irrigation treatments were 4.3 and 2.8% higher, and the grain yields were 5.8 and 3.3% higher, respectively, than those of the non-irrigated yields. Overall, applying a small amount of water between 12:00–14:00 with a center pivot before the occurrence of dry-hot wind is an effective means to regulate the field microclimate and produce more yield in the North China Plain.

Response of Tomato Sugar and Acid Metabolism and Fruit Quality under Different High Temperature and Relative Humidity Conditions

The combined stress of high temperature and high relative air humidity is one of the most serious agrometeorological disasters that restricts the production capacity of protected agriculture. However, there is little information about the precise interaction between them on tomato fruit quality. The objectives of this study were to explore the effects of the combined stress of high temperature and relative humidity on the sugar and acid metabolism and fruit quality of tomato fruits, and to determine the best relative air humidity for fruit quality under high temperature environments. Four temperature treatments (32°C, 35°C, 38°C, 41°C), three relative air humidity (50%, 70%, 90%) and four duration (3, 6, 9, 12 d) orthogonal experiments were conducted, with 28°C, 50% as control. The results showed that under high temperature and relative air humidity, the activity of sucrose metabolizing enzymes in young tomato fruits changed, which reduced fruits soluble sugar content; in addition, enzyme activities involved phosphopyruvate carboxylase (PEPC), mitochondria aconitase (MDH) and citrate synthetase (CS) increased which increased the content of organic acids (especially malic acid). Eventually, vitamin C, total sugar and sugar-acid ratio decreased significantly, while the titratable acid increased, resulting in a decrease in fruit flavor quality and nutritional quality in ripe fruit. Specifically, a temperature of 32°C and a relative air humidity of 70% were the best cultivation conditions for tomato reproductive growth period under high temperature. Our results indicating that fruit quality reduced under high temperature at the flowering stage, while increasing the relative air humidity to 70% could alleviate this negative effect. Our results are benefit to better understand the interaction between microclimate parameters under specific climatic conditions in the greenhouse environment and their impact on tomato flavor quality.

Excessive Rainfall Is the Key Meteorological Limiting Factor for Winter Wheat Yield in the Middle and Lower Reaches of the Yangtze River

In the era of global climate change, extreme weather events frequently occur. Many kinds of agro-meteorological disasters that are closely related to environmental conditions (such as sunshine hours, temperature, precipitation, etc.) are witnessed all over the word. However, which factor dominates winter wheat production in the middle and lower reaches of the Yangtze River remains unresolved. Quantifying the key limiting meteorological factor could deepen our understanding of the impact of climate change on crops and then help us to formulate disaster prevention and mitigation measures. However, the relative role of precipitation, sunshine hours and maximum daily temperature in limiting winter wheat yield in the middle and lower reaches of the Yangtze River is not clear and difficult to decouple. In this study, we used statistical methods to quantify the effect of precipitation, maximum temperature and sunshine hours extremes on winter wheat (Triticum aestivum L.) yield based on long time-series, county-level yield data and a daily meteorological dataset. According to the winter wheat growing season period (October of the sowing year to May of the following year), anomaly values of cumulative precipitation, average sunshine hours and average daily maximum temperature are calculated. With the range of −3 σ to 3 σ of anomaly and an interval of 0.5 σ (σ is the corresponding standard deviation of cumulative precipitation, mean maximum temperature and mean sunshine hours, respectively), the corresponding weighted yield loss ratio (WYLR) represents the impact of this kind of climate condition on yield. The results show that excessive rainfall is the key limiting meteorological factor that can reduce winter wheat yield to −18.4% in the middle and lower reaches of the Yangtze River, while it is only −0.24% in extreme dry conditions. Moreover, yield loss under extreme temperature and sunshine hours are negligible (−0.66% for extremely long sunshine hours and −8.29% for extreme cold). More detailed analysis results show that the impact of excessive rainfall on winter wheat yield varies regionally, as it causes severe yield reductions in the Huai River basin and the middle to southern part with low elevation and rainy areas of the study area, while for drier areas in the Hubei province, there is even an increase in yield. Our results disclosed with observational evidence that excessive precipitation is the key meteorological limiting factor leading to the reduction in winter wheat yield in the middle and lower reaches of the Yangtze River. The knowledge of the possible impact of climate change on winter wheat yield in the study area allows policy-makers, agronomists and economists to better forecast a plan that differs from the past. In addition, our results emphasized the need for better understanding and further process-based model simulation of the excessive rainfall impact on crop yield.

BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data.

Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are commonly missing, and an appropriate data imputation method is necessary in meteorological data applications. In this paper, we focus on filling long gaps in meteorological observation data at field sites. A deep learning-based model, BiLSTM-I, is proposed to impute missing half-hourly temperature observations with high accuracy by considering temperature observations obtained manually at a low frequency. An encoder-decoder structure is adopted by BiLSTM-I, which is conducive to fully learning the potential distribution pattern of data. In addition, the BiLSTM-I model error function incorporates the difference between the final estimates and true observations. Therefore, the error function evaluates the imputation results more directly, and the model convergence error and the imputation accuracy are directly related, thus ensuring that the imputation error can be minimized at the time the model converges. The experimental analysis results show that the BiLSTM-I model designed in this paper is superior to other methods. For a test set with a time interval gap of 30 days, or a time interval gap of 60 days, the root mean square errors (RMSEs) remain stable, indicating the model’s excellent generalization ability for different missing value gaps. Although the model is only applied to temperature data imputation in this study, it also has the potential to be applied to other meteorological dataset-filling scenarios.

RETRACTED ARTICLE: The role and impact of environmental big data in agricultural disaster management

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.

Characteristics and Preventive Measures of Agrometeorological Disasters

Characteristics and Preventive Measures of Agrometeorological Disasters

Identification and characteristics of combined agrometeorological disasters caused by low temperature in a rice growing region in Liaoning Province, China

Owing to climate change, agrometeorological disasters are becoming increasingly complex. Here, we analysed the characteristics of combined agrometeorological disaster (CAD) caused by low temperature in annual rice crops in Liaoning Province, China, from 1961 to 2017. We assessed the repeat occurrence of natural disasters on rice production. The results showed that (1) there were six possible CAD scenarios in a rice growing season. These included two scenarios with one disaster in two periods (OD-1, OD-2), three scenarios with two different disasters (TD-1, TD-2, TD-3) and one with multiple disasters (MD-1). Since 1961, the overall occurrence of the six CAD scenarios showed a downward trend. Among the six scenarios, TD-1 had the greatest distribution and occurred most frequently; (2) three possible single agrometeorological disaster (SAD) scenarios may occur during a rice growing season, delayed cold damage (SAD-d), frost damage at only one stage (SAD-f), sterile-type cold damage at one stage (SAD-s). Since 1961, the SAD-d frequency decreased, whereas, since the mid-1980s, the SAD-f frequency increased; (3) SAD and CAD frequencies showed downward trends, with CAD declining more than SAD. The CAD geographical range and frequency were smaller than those of SAD. Rice damage in SAD-f and OD-1 scenarios showed no significant trend, but appeared to have slightly increased. The main agrometeorological disasters affecting rice production in Liaoning Province were delayed cold damage, frost damage or both; (4) a comparison of the rice yield reduction rates of years in which CAD or SAD occurred in more than 50% of stations in Liaoning Province revealed that the yield reduction rates associated with the former were greater than those associated with the latter. CAD had more types, and the occurrences and impacts were more complicated, than for SAD. Compared with SAD, the effects of CAD may be magnified in rice crops, leading to reduced yields.

Spatiotemporal variations in damages to cropland from agrometeorological disasters in mainland China during 1978–2018

Drought, flood, hail, low temperature, and frost (LTF) are the main agrometeorological disasters (AMDs) in China; however, comprehensive and quantitative studies on cropland damage induced by AMDs across the whole country in terms of long-term trends are still lacking and urgently needed. Based on historical statistical data from yearbooks and bulletins, the overall characteristics of cropland damage by AMDs during 1978–2018 were analyzed using a pre-whitening procedure and a Mann-Kendall trend test at yearly and provincial scales in China. The results showed that drought was the most severe, with an average covered area of 22.2 million ha and an affected area of 11.2 million ha every year during 1978–2018, followed by flood, hail, and LTF. A decreasing trend was observed in covered area and affected area by drought, flood, and hail, while only LTF showed an increasing trend. On provincial scale, more than 70% of the covered area by AMDs was induced by drought and flood in most provincial districts. In all provincial districts of northern China, more than 50% of the covered area was induced by drought. In most provincial districts of southern China, more than 40% of the covered area was induced by flood. Hail disasters were prominent in Xinjiang, with significant increasing trends among all parameters. Compared with the other three AMDs, LTF covered and affected the smallest cropland area, but significant increasing trends were observed in the northwest and middle parts of China. The results of this study systematically display the characteristics of damage to cropland by four main AMDs, which are critical and necessary for disaster risk reduction and adaptive strategy development.

Research on Monitoring and Forecasting Technology of Agro-meteorological Disasters

Research on Monitoring and Forecasting Technology of Agro-meteorological Disasters

Identification and characteristics of multiple agrometeorological disaster of maize in Liaoning Province, China

Multiple agrometeorological disaster (MAD) occurs simultaneously in maize production. In order to ascertain the occurrence regularity and characteristics of MAD of maize in Liaoning Pro-vince, we defined and classified MAD, identified MAD of 50 meteorological stations in the maize growing season of Liaoning Province from 1961 to 2017, and examined the effects of MAD on maize yield in typical years. The results showed that the occurrence range of MAD was higher in most years from 1961 to 2017 than that of single agrometeorological disaster (SAD), and that the occurrence frequency of MAD was higher in most areas than that of SAD. The major MAD in Liaoning Province was drought in multiple periods (M1-D), followed by the combination of drought and delayed cold damage (M2-DC). The occurrence range of M1-D showed an upward trend from 1961 to 2017, whereas other MAD types showed a downward trend. After analyzing the occurrence of MAD in the typical years of maize yield reduction, we found that the occurrence frequency of M1-D and M2-DC was higher.

Multidimensional research on agrometeorological disasters based on grey BP neural network

PurposeThe purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a scientific and reasonable system of agrometeorological disasters prevention and reduction and guaranteeing grain security.Design/methodology/approachFirstly, according to the statistical data of areas covered by natural disaster, areas affected by natural disaster, sown area of grain crops and output of grain crops from 1979 to 2018 in Henan Province, China. We have constructed an agrometeorological disaster risk assessment system for Henan province, China, which is composed of indicators such as rate covered by natural disaster, rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability. The variation characteristics of agrometeorological disasters in Henan Province and their effects on agricultural production are analyzed. Secondly, the grey relational analysis method is used to analyze the relation degree between the main agrometeorological disaster factors and the output of grain crops of Henan Province. Based on the grey BP neural network, the rate covered by various natural disaster and the rate affected by various natural disaster are simulated and predicted.FindingsThe results show that: (1) the freeze injury in the study period has a greater contingency, the intensity of the disaster is also greater, followed by floods. Droughts, windstorm and hail are Henan Province normal disasters. (2) According to the degree of disaster vulnerability, the ability to resist agricultural disasters in Henan Province is weak. (3) During the study period, drought and flood are the key agrometeorological disasters affecting the grain output of Henan Province, China.Practical implicationsThe systematic analysis and evaluation of agrometeorological disasters are conducive to the sustainable development of agriculture, and at the same time, it can provide appropriate and effective measures for the assessment and reduction of economic losses and risks.Originality/valueBy calculating and analyzing the rate covered by natural disaster, the rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability of crops in Henan Province of China and using grey BP neural network simulation projections for the rate covered by various natural disaster and the rate affected by various natural disaster, the risk assessment system of agrometeorological disasters in Henan is constructed, which provides a scientific basis for systematic analysis and evaluation of agrometeorological disasters.

Discussion on Remote Sensing Monitoring of Agrometeorological Disasters

<p>The continuous development of modern society and economy puts forward higher requirements for agricultural production in China. Traditional monitoring technology can no longer meet the needs of agrometeorological disaster monitoring. Scientific application of remote sensing monitoring technology has extremely important value, which can effectively improve the detection effect, make it more accurate and real-time, and then promote China’s modern agricultural production. This article analyzes the application value of remote sensing monitoring technology. Based on this, it comprehensively explores the specific situation of monitoring different disasters by remote sensing monitoring technology in the agricultural production process.</p>

Responses of Yield Fluctuation of Winter Oilseed Rape to Climate Anomalies in South China at Provincial Scale

The regional differences for responses of winter oilseed rape yield to climate changes was not reported in China, and the statistical climate–yield relationships at the provincial scale in south China should be a valuable attempt. With using provincial data of climate variables and oilseed rape yields during 1978–2016, the main findings were as follows: (1) based on 14 provincial climate-induced yield index (CIYI) series for oilseed rape yield, south China could be divided into five sub-regions with different temporal variations in CIYI by using principal component analysis, and they were Yangtze River delta (Region I), central south China (Region II), southwest China (Region III), southernmost China (Region IV) and southeastern coast (Region V), respectively; (2) the correlation and regression analysis between CIYI and monthly climate variables indicated that the precipitation of April, average potential evapotranspiration of November, average potential evapotranspiration of October, average mean temperature of May, and average potential evapotranspiration of September were the key climate variables (KCV) affecting yield in Region I, II, III, IV and V, respectively; (4) among the five sub-regions, the impacts of KCV changes on yields in southwest China were more notable, and the increasing trend of average potential evapotranspiration of November caused the decrease of yield by 2.08% in 1990s and 4.24% after 2000, respectively. These results could be employed as references for the assessment and mitigation of agro-meteorological disasters in south China.

西南地区主要农业气象灾害时空变化特征及趋势分析

西南地区主要农业气象灾害时空变化特征及趋势分析

Multi-risk assessment of agrometeorological disasters based on a three-level hierarchical framework

Multi-risk assessment of agrometeorological disasters based on a three-level hierarchical framework

Mapping high temperature damaged area of paddy rice along the Yangtze River using Moderate Resolution Imaging Spectroradiometer data

ABSTRACT The subtropical high temperature anomaly of 2013 in China, adversely affected paddy rice physiology, especially at the heading, flowering and grain filling stages, and left untold damage and significant rice yield reductions in that year. Considering the importance of data on crop failure induced by abnormal meteorological events, in understanding the potential impacts of climate change on agriculture and food security, and in support of crop insurance policies, the current study employs satellite remote sensing data to map and estimate the areal extent of high-temperature-induced damage on paddy rice in 2013, along the Yangtze River, a major rice producing area in China. In this regard, the daily mean and maximum air temperatures were first estimated using the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products, MODIS Vegetation Indices (VI) products, altitude information from a Digital Elevation Model (DEM), and air temperature measurements at meteorological stations located within the Yangtze River Basin from 2000 to 2016, to derive fully covered mean and maximum daily air temperatures. Paddy rice cultivated areas were then identified based on the seasonal profiles of MODIS Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) in 2013, and map errors at provincial scales were no more than 8.7%. Finally, paddy rice growth characteristics were combined with MODIS VI products to identify heading and flowering stages, and the time series of daily mean and maximum air temperatures, paddy rice phenology and cultivated area, were integrated to map and estimate the rice crop area damaged by the anomalous high temperature. Our results show that all provinces along the Yangtze River, this study investigated, recorded a substantial damaged rice crop area, following the 2013 subtropical temperature high, and Anhui Province was the hardest hit with a damaged rice area of 63.6%. For the entire study area, a damage extent of about 5,656,000 ha, accounting for 35.9% of total paddy rice planted area, was recorded. This indicates a significant agro-meteorological disaster, and in an era of increasing climate variability and weather extremes, studies of this nature would greatly benefit global food security planning and management policies.

Quantifying the effect of temporal variability of agro-meteorological disasters on winter oilseed rape yield: a case study in Jiangsu province, southeast China.

To address the increasing hazards of climate change to winter oilseed rape production in Jiangsu province, southeast China, a total of 12 agro-meteorological disaster indices (MDI) during 1961-2012 was applied to quantify the disaster impacts of spring precipitation extremes, autumn droughts, winter freezing injuries, and spring chilling injuries. The main findings were as follows: (1) due to the obvious north-south gradient of MDI, this province was classified as four climactic sub-regions: north, central-north, central-south, and south Jiangsu, respectively; (2) in central-north, central-south, and south Jiangsu, the maximum length of dry spell during autumn (DI3), the maximum duration of consecutive rainfalls during spring (PI3), and number of rainy days during spring (PI1) were selected as the leading MDI affecting oilseed rape yield, respectively; (3) each 1d of DI3, PI3, and PI1 increase would result in yield losses of 0.7%, 3.6%, and 1.7% in central-north, central-south, and south Jiangsu, respectively; (4) compared to the weak changes in spring rainfall and autumn drought, the significant weakening of winter-spring cold extremes across Jiangsu had more notable yield increasing effects, especially the strong decrease of freezing events during winter had increased yield by up to 1.4-3.0%.

Evaluating agro-meteorological disaster of China based on differential evolution algorithm and VIKOR

Agriculture is not only concerned with the daily life of the local residents, but also plays a significant role in the sustainable development of China and even the stability of the world grain market. The evaluation of meteorological disaster involves many indexes; it can be regarded as a multiple criterion decision-making problem. Comprehensive indexes system of agro-meteorological disaster is proposed. The relative weights of the indexes are acquired by differential evolution algorithm. A novel evaluation model based on VIKOR is proposed to estimate the agro-meteorological disaster of China from 2006 to 2010. The experiment results have demonstrated that the situation of agro-meteorological disaster in North and Northwest area of China was severe and the agro-meteorological disaster of 2008 was very serious.