Agricultural Disaster Research Articles

SUPERVISED CLASSIFICATION METHODS FOR AUTOMATIC DAMAGE DETECTION CAUSED BY HEAVY RAINFALL USING MULTITEMPORAL HIGH RESOLUTION OPTICAL IMAGERY AND AUXILIARY DATA

Abstract. In the context of climate change and rising frequency of extreme hydro-meteorological events around the world, flood risk management and mapping of heavy rainfall-related damages represent an ongoing critical challenge. For decades now, remote sensing has been largely used to investigate spatial and temporal changes in land use and water resources. Today, different satellite products provide fast and crucial knowledge for the study of hydrological disasters over large areas, possibly in remote regions, with high spatial resolution and high revisit frequency. Yet, until now, few works have sought to detect the full range of extreme rainfall-related damages with optical imagery, especially those caused by intense rainwater runoff beyond the direct vicinity of major waterways. The work presented in this paper focuses on the Aude severe weather event of October 15th, 2018, in the South of France, for which more than a thousand claims for agricultural disaster were registered, both related to river overflowing and rainwater runoff.The full resources of ground truths, contextual information, land use as well as digital elevation model (DEM) combined to high resolution and high frequency optical imagery (Sentinel-2, Pléiades) are used to develop an automatic damage detection method based on supervised classification algorithms. Through the combination of several indicators characterizing heterogeneous spectral variations among agricultural plots following the event, a Gaussian process classifier achieved various classification accuracies up to 90% on a large comparable and independent photo-interpreted validation sample. This work builds great expectations for applications in other areas with contrasted climate, topography and land cover.

Perception of agricultural drought resilience in South Africa: A case of smallholder livestock farmers.

Worldwide drought has significance and continues to pose long-lasting effects on the agricultural sector, including South Africa. The recurring drought is a major challenge to smallholder livestock farmers in the Northern Cape Province of South Africa. This study assesses the perception of smallholder livestock farmers towards agricultural drought resilience. The study utilised a perception index score using primary data collected from 207 smallholder livestock farmers following a structured questionnaire survey and multistage sampling procedures. The study found that the average perception index of the role of social networks and government to enhance agricultural drought resilience was negative, which implied that their role in enhancing resilience towards agricultural drought was insufficient. However, the perception of smallholder livestock farmers on the role of social networks was lower than the role of government. This study recommends coordination and cooperation amongst all role players to reinforce strategies to enhance smallholder livestock farmers’ resilience. This includes coordinator amongst the local, provincial government, African Farmers’ Association of South Africa, extension officers, private sectors, monitoring agencies in terms of reliable early warning information and communication amongst decision-makers. Collaboration amongst government departments at the national and provincial levels should be strengthening to enhance farmer’s resilience. The collaboration includes the Department of Agriculture, Forestry and Fisheries at the national level, Provincial Departments of Agriculture, National and Provincial Disaster Management Centres, South African Weather Service and Department of Water Affairs. Smallholder livestock farmers’ awareness of the significance of social networking and government participation should be promoted.

Measurement and impactor analysis of agricultural carbon emission performance in Changjiang economic corridor

Based on the panel data of Changjiang Economic Corridor (CEC), this paper includes agricultural carbon emissions as an unwanted output into the evaluation index system for agricultural carbon emissions performance (ACEP), and measured the ACEP of the 11 provinces in the CEC of 2005–2018, using the minimum distance to weak efficient frontier (MinDW) model. In addition, a Tobit model was constructed for empirical analysis on the impactors of CEC ACEP. The results show that: In general, the CEC achieved an excellent ACEP, with certain differences between the provinces; the ACEP trends varied between the upper, middle, and lower reaches of the CEC, and the lower reaches had a much higher ACEP than the upper and middle reaches; the results of Tobit model demonstrate that industrial upgrading and rural power consumption have significant positive correlations with CEC ACEP, while human capital, planting structure, and agricultural disaster have significantly negative correlations with CEC ACEP.

Improving rural disaster response preparedness.

Disasters happen in all communities and negatively impact the health and safety of populations if not well managed. Rural health organizations face greater challenges when implementing emergency preparedness policies and need unique resources to help ensure the health of their communities. The purpose of this article is to describe the development and evaluation of an agricultural simulation scenario designed for rural health organizations. The agricultural disaster scenario was developed after determining the desired content domains. Forty students from nursing, medicine, public health, and social work participated in a functional simulation using the scenario. Outcomes were measured using a pre-post measurement with one cohort design. Data were analyzed using paired-samples t-tests. Students reported a 30% increase in self-confidence, 38% increase in disaster competence, and 19% increase in interprofessional collaboration competence after the simulation. Statistically significant increases in student scores were noted on all surveys across professions. The agricultural disaster simulation is a valid scenario to use for disaster preparation using emergency operations plans.

The locust plagues of the Ming and Qing dynasties in the Xiang-E-Gan region, China

Based on the historical documents of Ming and Qing dynasties, with the help of mathematical statistics and the ArcGIS spatial analysis method, this study reconstructed grades and counties affected by locust plagues to examine their spatiotemporal characteristics, environmental significance and social impact. The results show that: (1) There were 242 locust plague years in the Xiang-E-Gan region during the Ming and Qing dynasties, with an average of one outbreak every 2.25 years. The Qing dynasty suffered more than the Ming dynasty, and Hubei Province was affected more seriously than Jiangxi Province and Hunan Province. Locust plagues mostly occurred from March to August, and autumn locust plagues occurred more frequently than summer locust plagues. (2) The sixteenth, seventeenth and nineteenth centuries were periods of successive outbreaks, with 4.13 locust plague years and 10.75 affected counties per decade. The spatial heterogeneity and aggregation of locust outbreaks were significant, and the plain area was affected more seriously and continuously. (3) Low temperature promoted the outbreak of locust plagues, especially during the period of high occurrence, and the outbreaks cannot indicate the previous year’s temperature. Compared with floods, the relationship between droughts and locust plague was closer, and large-scale climate change may also affect locust outbreaks. (4) Long-term natural disasters multiplied the food price and food prices lagged behind disasters. Epidemics were often accompanied by locust plagues, forming a typical chain of agricultural disasters that did great harm to the society.

Spatiotemporal drought analysis by the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in Sichuan Province, China

Drought refers to a meteorological disaster that causes insufficient soil moisture and damage to crop water balance due to long-term lack of precipitation. With the increasing shortage of water resources, drought has become one of the hot issues of global concern. The standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) can effectively reflect the changes in drought characteristics of different geomorphologies in Sichuan on time and space scales, to explore the difference in drought characteristics between different physiognomy types in Sichuan Province, We calculated the SPI and SPEI values based on the data of 44 meteorological stations in Sichuan Province from 1961 to 2019 and used Mann–Kendall trend test and multivariable linear regression method (MLR) to quantify the significance of the drought characteristic trends at different time and space scales. The results as follow: (1) The SPEI drought trend in plain and hilly regions was greater than that in plateau and mountain regions on all time scales (− 0.039 year−1 for 1-month in hilly, − 0.035 year−1 for 1-month in plain, − 0.14 year−1 for 1-month in plateau, − 0.026 year−1 for 1-month in mountain) and the magnitude of trend of eastern (− 4.4 to 0.1 year−1) was lager than western (− 2.1 to 2.7 year−1), means that the drought trends transfer from northwest to east. (2) The drought intensity in the western region gradually increased (0.54–1.05) and drought events mainly occurred in the southwest plateau and central mountainous regions (24–47 times), means that drought meteorological hotspots were mainly concentrated in the Sichuan basin. (3) The MLR indicated altitude (H) is not the main influencing factor that causes the spatial unevenness of precipitation in Sichuan Province, but altitude (H), temperature (T), longitude (Lo) and latitude (La) can co-determined the precipitation. The results of this study are instructive and practical for drought assessment, risk management and application decision-making in Sichuan Province, and have guiding significance for agricultural disaster prevention, mitigation and agricultural irrigation in Sichuan Province.

A Study on Establishment of Drought Index for Agricultural Disaster Prediction

This study aims to establish a drought index for disaster prediction in Gyeongsangnam-do, where the most agricultural drought damage occurred from 1965 to 2018. The drought index was analyzed for each duration (3, 6, 9, 12 months) targeting the SPI. Damage characteristics of the duration of agricultural drought were calculated. SPI for each duration of agricultural drought damage period in Gyeongsangnam-do was at least -2.0 or less, and the maximum was -1.0 or more, and weak and moderate drought were analyzed. However, due to the heavy rain effect during the rainy season, the average SPI12 was -1.06, and the impact of agricultural drought was negligible. It was analyzed that the correlation between the damage period of agricultural drought and the SPI by duration was high. However, there is not much difference in SPI for each duration to determine the occurrence of damage. In this study, the criterion for disaster prediction of agricultural drought was calculated as representative drought index by year as the minimum drought index of SPI for each duration of damage occurrence period of past agricultural drought. The Standard of drought index for disaster prediction was set to -1.64, the average of the SPI for each duration of year in which damage occurred in the past.

Food in space from hydrogen-oxidizing bacteria

The cost of launching food into space is very high. An alternative is to make food during missions using methods such as artificial light photosynthesis, greenhouse, nonbiological synthesis of food, electric bacteria, and hydrogen-oxidizing bacteria (HOB). This study compares prepackaged food, artificial light microalgae, and HOB. The dominant factor for each alternative is its relative mass due to high fuel cost needed to launch a payload into space. Thus, alternatives were evaluated using an equivalent system mass (ESM) technique developed by the National Aeronautics and Space Administration. Three distinct missions with a crew of 5 for a duration of 3 years were analyzed; including the International Space Station (ISS), the Moon, and Mars. The components of ESM considered were apparent mass, heat rejection, power, and pressurized volume. The selected power source for all systems was nuclear power. Electricity to biomass efficiencies were calculated for space to be 18% and 4.0% for HOB and microalgae, respectively. This study indicates that growing HOB is the least expensive alternative. The ESM of the HOB is on average a factor of 2.8 and 5.5 less than prepackaged food and microalgae, respectively. This alternative food study also relates to feeding Earth during a global agricultural catastrophe. Benefits of HOB include recycling wastes including CO2 and producing O2. Practical systems would involve a variety of food sources.

Crisis Communications in a Natural Agricultural Disaster

Crisis Communications in a Natural Agricultural Disaster

Enhancing the ability of agriculture to cope with major crises or disasters: What the experience of COVID-19 teaches us

The COVID-19 outbreak was an unprecedented situation that uncovered forgotten interconnections and interdependencies between agriculture, society, and economy, whereas it also brought to the fore the vulnerability of agrifood production to external disturbances. Building upon the ongoing experience of the COVID-19 pandemic, in this short communication, we discuss three potential mechanisms that, in our opinion, can mitigate the impacts of major crises or disasters in agriculture: resilience-promoting policies, community marketing schemes, and smart farming technology. We argue that resilience-promoting policies should focus on the development of crisis management plans and enhance farmers' capacity to cope with external disturbances. We also stress the need to promote community marketing conduits that ensure an income floor for farmers while in parallel facilitating consumer access to agrifood products when mainstream distribution channels under-serve them. Finally, we discuss some issues that need to be solved to ensure that smart technology and big data can help farmers overcome external shocks.

Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine

To meet the challenges of climate change, increasing population and food demand, a timely, accurate and reliable estimation of crop yield at a large scale is more imperative than ever for crop management, food security evaluation, food trade and policy-making. In this study, taking the major winter wheat production regions of China as an example, we compared a traditional machine learning method (random forest, RF) and three deep learning (DL) models, including DNN (deep neural networks), 1D-CNN (1D convolutional neural networks), and LSTM (long short-term memory networks) to predict crop yields by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data. The results showed that all four models could capture winter wheat yield variations in all the county-years, with R2 of recorded and simulated yields ranging from 0.83 to 0.90 and RMSE ranging from 561.18 to 959.62 kg/ha. They all performed well for winter wheat yield prediction at a county level from 2011 to 2015, with mean R2≥0.85 and RMSE ≤ 768 kg/ha. At a field level, the spatial pattern of estimated winter wheat yield could capture the spatial heterogeneity and yield differences between individual fields across a county fairly well. However, only the DNN and RF models had relatively good performance at the field level, with mean R2 values of 0.71, 0.66 and RMSE values of 1127 kg/ha and 956 kg/ha, respectively. The model comparisons showed that the performance of RF was not always worse than DL at both the county and field levels. Our findings demonstrated a scalable, simple and inexpensive framework for estimating crop yields at various scales in a timely manner and with reliable accuracy, which has important implications for crop yield forecasting, agricultural disaster monitoring, food trade policy, and food security warning.

Multidimensional Assessment of Food Provisioning Ecosystem Services Using Remote Sensing and Agricultural Statistics

With the increasing global population, human demands for natural resources continue to grow. There is a critical need for the sustainable use and development of natural resources. In this context, ecosystem services have attracted more and more attention, and ecosystem services assessment has proven to be useful for guiding research, policy formulation, and management implementation. In this paper, we attempted to assess ecosystem services more comprehensively from various perspectives. We used food provisioning ecosystem services in Minnesota as a case study and proposed two new concepts for assessing ecosystem services: efficiency and trend. We designed a multidimensional assessment framework, analyzed the total output, efficiency, and trend temporally based on both area and space with Exploratory Spatial Data Analysis (ESDA). We also identified major influencing factors based on remote sensing images in Google Earth Engine and explored the quantitative influence on each assessment dimension. We found that: (1) Food provisioning ecosystem service in Minnesota has generally been improving from 1998 to 2018. (2) We identified food provisioning ecosystem services in Minnesota as superior zones, mixed zones, and inferior zones with a ‘sandwich geo-configuration’. (3) The total output tends to be stable while the efficiency is disturbed by some natural disasters. Simultaneously, the trend index has been improving with slight fluctuations. (4) Agricultural disaster financial support has a stronger impact on stabilizing the total output of food provisioning than the other two dimensions. (5) Soil moisture, diurnal temperature difference, and crop growth are the three main influencing aspects of food provisioning ecosystem services, and the order of the influential density is: the Perpendicular Drought Index (PDI), Normalized Difference Vegetation Index (NDVI), Rainfall (RF), Daytime Temperature (DT), and Diurnal Temperature Difference (DIF).

Call for Special Issue Papers: Big Data Analytics for Agricultural Disaster Management.

Call for Special Issue Papers: Big Data Analytics for Agricultural Disaster Management.

The flash drought of 1936

An exceptional flash drought during the spring and summer of 1936 led to extreme heat waves, large losses of human life and significant reductions of crop production. An analysis of historic precipitation and temperature records shows that the flash drought originated over the southeastern United States (U.S.) in April 1936. The flash drought then spread north and westward through the early summer of 1936 and possibly merged with a flash drought that had developed in the spring over the northern Plains. The timing of the flash drought was particularly ill-timed as most locations were at or entering their climatological peak for precipitation at the onset of flash drought, thus maximizing the deficits of precipitation. Thus, by early July most locations in the central and eastern U.S. were either in drought or rapidly cascading toward drought. The weeks that followed the 1st of July were some of the hottest on record in the U.S., with two major heat waves: first over the Midwest and eastern U.S. in the first half of July and then across the south-central U.S in the month of August. The combination of the flash drought and heat wave led to an agricultural disaster in the north central U.S. and one of the deadliest events in U.S. history.

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.

Introduction to the Special Issue “Gender and Development in Rural Communities and Agriculture”

Abstract Gender inequality and women’s empowerment are on the agenda of many governments, businesses, and most major international development agencies and organizations today. Despite this emphasis, there is growing concern among many feminists that the “mainstreaming” of gender within existing institutions has undermined the transformative potential of these concepts and practices (Collins 2021; Cornwall and Rivas 2015; Farhall and Rickards 2021). Within this context, feminist scholarly and applied work remains a priority for those seeking to reduce gender inequality and ensure women’s empowerment. This special issue seeks to contribute to these efforts. The authors in this special issue illuminate key areas related to agriculture and rural communities, including: agricultural technologies, disasters and climate change, maternal mortality, microfinance, and rising rates of global hunger. They demonstrate that gender inequalities and women’s empowerment remain challenging because of the multifaceted nature and ways in which these inequalities and power imbalances are embedded in specific social relationships. At the same time, these authors provide important empirical evidence and theoretical framings that can aid future scholarly efforts to expand our understandings of how we can enhance the strategic capacity of women to build positive change in this area.

Effects of water-saving technology application, disaster type and occurrence stage on disaster area of cotton field

The purpose of this paper is to analyze the impact of the application of water-saving technology, types of agricultural disasters and disaster stages on the affected area of cotton field by using the survey data. In this study, bootstrap method was used for quantile regression analysis of cotton disaster area. Four quantiles were selected in the analysis, which was 0.25, 0.5, 0.75 and 0.90. The results were compared with those of OLS regression analysis. The results indicate that the application of drip irrigation water-saving technology and the increase of irrigation expenditure have obvious negative correlation with cotton disaster area. Wind disaster, hail disaster, rain and waterlogging, drought, freezing damage and insect pests have obvious positive effects on the affected area of cotton, and the disease has no significant impact on the affected area of cotton field because of its strong controllability. The results of the study estimated the impact of water-saving technology and disaster types on cotton field disaster area in detail.

Identification of Corn Seeds with Different Freezing Damage Degree Based on Hyperspectral Reflectance Imaging and Deep Learning Method

Seed freezing damage is an agricultural disaster, which has a great impact on seed quality assurance. The feasibility of combining hyperspectral imaging with deep convolutional neural network (DCNN) to classify different freeze-damaged corn seeds was studied in this paper. At first, the hyperspectral images of corn seeds subjected to five different freezing temperatures at 400–1000 nm were acquired, and then the average spectra were extracted from the region of embryo hyperspectral images over the wavelength range of 450–979 nm. Next, four models (K nearest neighbors (KNN), support vector machine (SVM), extreme learning machine (ELM), and DCNN) were developed for five-category (5 frozen conditions) and four-category (“no freezing,” “slight freezing,” “moderate freezing,” and “severe freezing”) classifications, and the values of the evaluation indexes (accuracy, sensitivity, specificity, and precision) were calculated for comparison. The results show that DCNN model had the most satisfactory result with accuracy rates of 100% (training set), 96.9% (validation set), and 97.5% (testing set) for five-category classification, with accuracy rates of 100% (training, validation, and testing set) for four-category classification, and DCNN model also had the best performance in the evaluation indexes. At last, the visual classification map was generated according to the results of DCNN. It shows that hyperspectral imaging and DCNN can provide a novel method to detect the freezing damage of corn seeds quickly and inexpensively.

Volcanic eruptions, successive poor harvests and social resilience over southwest China during the 18–19th century

Volcanic eruptions, climate changes and their influences on crop harvests and social development are of increasing concern in science communities. Using a dataset of crop harvest scores of southwest China from 1730 to 1910, which was derived from the memorials to the emperors in the Qing Dynasty of China, reconstructed climate proxies and the chronology of large volcanic eruptions occurring between 10°S and 15°N, we analysed possible relationships between crop harvests, climate changes and volcanic eruptions. In addition, some archives of policies and measures related to crops and social development extracted from the chronicles were used to analyse social resilience when faced with poor harvests. The results show that crop harvests in the study area generally increased with fluctuations when there were less low-latitude large volcanic eruptions from 1730 to 1810. However, from 1811 to 1910, volcanoes at low latitudes erupted more frequently, which contributed to concurrent low temperature and drought. Meanwhile, the crop harvests showed a step-down decrease during the following periods of 1810s, 1850s, 1870s and 1890s. Though, the local social system was certainly resilient in facing of such climate and agriculture disasters, i.e. the local society remained stable without significant famine, large-scale migration or social unrest until 1911. The strong resilience of local social systems owed largely to various relieving measures, such as, building barns, exempting or reducing local taxes, allocating farmland to immigrants, and central government dominated grain purchasing and distribution to alleviate disasters.

Incendios, inundaciones y sequías en la pampa entre 1998 y 2018. Una mirada desde la perspectiva del riesgo y desastre ambiental

In order to know the occurrence of three elements of risk and disaster such as fires, floods and droughts and how these affected La Pampa province between 1998 and 2018, the regulations and the Emergency and/or Agricultural Disaster declarations were analyzed. From the perspective of Risk and Environmental Disaster a total of 68 Emergency Decrees or Agricultural Disaster were issued during a 251 months period, corresponding 28 to droughts, 23 to floods, 16 to fires and 1 to temporal or rainstorm. The largest area of territorial affection was between January and March 2009 due to droughts affecting 14 millions of hectares (96.43% of La Pampa), and the longest period was 68 months (from January 1998 to May 2003) due to floods. It is observed that several urban center are located in the path of each analyzed adversity, which are direct or indirectly affected. As a prevention and a post­disaster action, important infrastructural and regulatory works has been carried out by La Pampa government. Despite that and the specialized vision regarding the agricultural sector, the lack of socialization of the problems and citizen participation in the decision making space persist