Agricultural Drought Vulnerability Research Articles

Multidimensional assessment of agricultural drought vulnerability based on socioeconomic and biophysical indicators

Agricultural drought threatens Türkiye's food security and economy, revealing a lack of multidimensional provincial vulnerability studies. Therefore, the primary objective of this study is to evaluate Türkiye's vulnerability to drought utilizing a multidimensional strategy incorporating biophysical and socio-economic indicators. In this context, an initial step involved the development of a conceptual framework for drought vulnerability, drawing upon the Intergovernmental Panel on Climate Change recommended elements of exposure, sensitivity, and adaptive capacity. Subsequently, this conceptual framework was applied at the provincial level, encompassing parameters such as temperature, precipitation, soil characteristics, water resources, agricultural earnings, demographic traits, educational levels, innovation, insurance coverage, and road density.The Aridity Index was used as an indicator for drought exposure, representing the physical dimension of drought. Under the sensitivity component, a total of 14 variables were defined under the indicators of “soil”, “water and land resources”, “economy”, and “population”, while under the adaptive capacity component, a total of five variables were identified. The weights of these indicators and variables were determined based on expert opinions using the Analytic Hierarchy Process method. As a result, five different indices were obtained by combining the indicators: Exposure Index, Sensitivity Index, Adaptation Capacity Index, Potential Drought Impact Index, and Agricultural Drought Vulnerability Index. In addition, spatial distribution models of these indices were also generated. Research results showed that the climatic regions most exposed to drought are also the most vulnerable. The Central, Southeastern, and Eastern Anatolian regions constitute the regions where the most vulnerable provinces are concentrated spatially.

Drought vulnerability of central Sahel agrosystems: a modelling-approach based on magnitudes of changes and machine learning techniques

ABSTRACT Agricultural drought is a complex phenomenon with numerous consequences and negative implications for agriculture and food systems. The Sahel is frequently affected by severe droughts, leading to significant losses in agricultural yields. Consequently, assessing vulnerability to agricultural drought is essential for strengthening early warning systems. The aim of this study is to develop a new multivariate agricultural drought vulnerability index (MADVI) that combines static and dynamic factors extracted from satellite data. First, pixel temporal regression from 1981 to 2021 was applied to climatic and biophysical covariates to determine the gradients of trend magnitudes. Second, principal component analysis was applied to groups of factors that indicate the same type of vulnerability to configure the basic equation of vulnerability to agricultural drought. Then, random forest (RF), K-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) were used to predict drought vulnerability classes using the 28 factors as inputs and 708 pts of randomly distributed class labels. The results showed statistical agreement between the predicted MADVI spatial variability and the reference model (R=0.86 for RF) and its statistical relationships with the vulnerability subcomponents, with an R=0.73 with exposure to climate risk, R=0.64 with the socioeconomic sensitivity index, R=0.6 with the biophysical sensitivity index and a relatively weak correlation (R=0.21) with the physiographic sensitivity index. The overall vulnerability situation in the watershed is 21.8% extreme, 10% very high, 16.8% high, 27.7% moderate, 22.2% low and 1.5% relatively low considering the cartographic results of the predicted vulnerability classes with SVM having the best performance (accuracy=0.96, Kappa=0.95). The study is the first approach that uses the gradients of magnitudes of satellite covariate anomaly trends in multivariate modelling of vulnerability to agricultural drought. It can be easily scaled up across the Sahel region to improve early warning measures related to the impacts of agricultural drought.

Nexus between Social Vulnerability and Resilience to Agricultural Drought amongst South African Smallholder Livestock Households

Livestock farmers in Sub-Saharan Africa rely on rain-fed agriculture, which exposes them to the risks of agricultural drought. Agricultural drought has become a significant threat to the extreme mortality of livestock, thus negatively impacting social vulnerability and household resilience to agricultural drought and extreme events. Researchers rarely empirically assess the connection between vulnerability and resilience, which are highly related concepts. By measuring and connecting vulnerability and resilience concepts closely related to disasters such as agricultural drought, this article makes a contribution to the body of disaster literature. The study aimed to empirically examine the relationship between smallholder livestock farming households’ social vulnerability and their resilience to agricultural drought. A survey of 217 smallholder livestock farmers was conducted. The Social Vulnerability Index (SVI), the Agricultural Drought Resilience Index (ADRI), and Pearson’s correlation coefficient were used for data analysis. A correlation was identified between resilience to agricultural drought and social vulnerability, indicating that smallholder livestock farmers are more susceptible to harm and lack the means to rebound effectively. Unsurprisingly, the majority of resource-poor smallholder livestock farmers (79%) lack safety nets during agricultural droughts. They are less resilient and more vulnerable households, leading them to social vulnerability. This study provides input/guidance to identify farming households with high social vulnerability and less resilience to threats and their capabilities of recouping and adopting after experiencing an agricultural drought. Additionally, looking at household resilience and social vulnerability to agricultural droughts could provide a way to pinpoint at-risk areas, assisting emergency planners in directing resources and intervention programs to those areas where assistance is most likely to be needed during disasters such as agricultural droughts. This implies that thorough policy intervention programs need to be tailored toward reducing damage or finding the path to recovery.

Assessment of Agricultural Drought Vulnerability Based on Crop Growth Stages: A Case Study of Huaibei Plain, China

Climate change can lead to and intensify drought disasters. Quantifying the vulnerability of disaster-affected elements is significant for understanding the mechanisms that transform drought intensity into eventual loss. This study proposed a growth-stage-based drought vulnerability index (GDVI) of soybean using meteorological, groundwater, land use, and field experiment data and crop growth model simulation. The CROPGRO-Soybean model was used to simulate crop growth and water deficit. Four growth stages were considered since the sensitivity of soybean to drought is strictly related to the growth stage. The GDVI was applied to the Huaibei Plain, Anhui Province, China, with the goal of quantifying the spatiotemporal characteristics of soybean drought vulnerability in typical years and growth stages. The results show that: (1) The sensitivity of leaf-related parameters exceeded that of other parameters during the vegetative growth stage, whereas the top weight and grain yield showed a higher sensitivity in the reproductive growth stage; (2) A semi-logarithmic law can describe the relationship between the drought sensitivity indicators and the GDVI during the four growth stages. The pod-filling phase is the most vulnerable stage for water deficit and with the highest loss upper limit (over 70%); (3) The 2001 and 2002 seasons were the driest time during 1997−2006. Fuyang and Huainan Cities were more vulnerable to drought than other regions on the Huaibei Plain in 2001, while Huaibei and Suzhou Cities were the most susceptible areas in 2002. The results could provide effective decision support for the categorization of areas vulnerable to droughts.

Assessment of Agricultural Drought Vulnerability with Focus on Upland Fields and Identification of Primary Management Areas

Robust water management systems are crucial for sustainable water use, particularly considering rapidly changing, ever-improving water supply system technologies. However, the establishment of specific management standards in upland fields is challenging, as several types of crops are cultivated in upland fields. Hence, the timing and required amount of water vary greatly, further rendering drought response challenging. In this study, we evaluated the agricultural drought vulnerability of South Korean upland fields, considering the lack of water resources, to establish preliminary drought damage prevention measures. The Technique for Order of Preference method was used for the drought vulnerability assessment, and the assessment indicators used were annual rainfall, number of dry days, upland field area, available soil water capacity, and groundwater usage. The 20 areas of highest vulnerability comprised large cultivation areas with minimal subsurface-water usage, except for areas where the number of dry days appeared to be the major factor for drought vulnerability. Damage caused by recurring droughts accumulated over time; thus, upland-field-oriented management may be required and can even be used in cases where insufficient drought information is available. Future studies can use the proposed method while considering assessment factors that describe upland field conditions.

Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms

The consequences of droughts are far-reaching, impacting the natural environment, water quality, public health, and accelerating economic losses. Applications of remote sensing techniques using satellite imageries can play an influential role in identifying drought severity (DS) and impacts for a broader range of areas. The Barind Tract (BT) is a region of Bangladesh located northwest of the country and considered one of the hottest, semi-arid, and drought-prone regions. This study aims to assess and predict the drought vulnerability over BT using Landsat satellite images from 1996 to 2031. Several indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Soil Moisture Content (SMC), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). VHI has been used to identify and predict DS based on VCI and TCI characteristics for 2026 and 2031 using Cellular Automata (CA)-Artificial Neural Network (ANN) algorithms. Results suggest an increasing patterns of DS accelerated by the reduction of healthy vegetation (19 %) and surface water bodies (26 %) and increased higher temperature (>5 °C) from 1996 to 2021. In addition, the VHI result signifies a massive increase in extreme drought conditions from 1996 (2 %) to 2021 (7 %). The DS prediction witnessed a possible expansion in extreme and severe drought conditions in 2026 (15 % and 13 %) and 2031 (18 % and 24 %). Understanding the possible impacts of drought will allow planners and decision-makers to initiate mitigating measures for enhancing the communities preparedness to cope with drought vulnerability.

Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India.

The amount of agricultural drought vulnerability in an underdeveloped rain-fed agro-based economy at the local, regional, and national level is most prominent factor for measurement. The desiccation of rain in agricultural sector becomes apprehensive to intercontinental food supply chain. So, adequate investigation and development of sustainable agricultural methodology are key factors to sustain the food security of a territory. In this research, delineation of agricultural drought vulnerability (ADV) status has been carried out by multidimensional mixed-method index approach using remote sensing and geographic information system. An integrated three-dimensional model is utilized to enrich this study. The three indices of this model include exposure index (EI), sensitivity index (SI), and adaptive capacity index (ACI). The ACI has been constructed by combining the environmental adaptive capacity (EAC), social adaptive capacity (SAC), and economic adaptive capacity (EcAC) index. The 40 parameters for ADV modeling are picked up by analyzing meteorological, geo-environmental, social, and remote sensing data. There are six exposure parameters, seven sensitivity parameters, twelve environmental adaptive capacity parameters, six social adaptive capacity parameters, and nine economic adaptive capacity parameters. Each index has been computed by assigning the weights based on their relative importance by using the analytic hierarchy process (AHP) approach. Final results were classified into five vulnerability zones, e.g., very low, low, moderate, high, and very high covering an area 362.32 km2, 186.68 km2, 568.69 km2, 547.05 km2, and 266.89 km2 respectively. Results have been validated with long-term Aman paddy yield data (2004 to 2014) through the yield anomaly index (YAI). Finally, the model ADV is a good model fit (R square = 0.894) and all the relationships were found significant, when SI, EI, and ACI are considered its predictors. While SI (B = 0.391, p < 0.001) and EI (B = 0.223, p < 0.001) are positively associated with ADV, ACI is negatively associated with ADV (B = - 0.721, p < 0.001). This regional agricultural drought vulnerability model can be useful to identify drought-responsive areas and improve drought mitigation measures.

Three-Dimensional Vulnerability Assessment of Peanut (Arachis hypogaea) Based on Comprehensive Drought Index and Vulnerability Surface: A Case Study of Shandong Province, China

Agricultural drought is a major problem facing China’s agricultural production. In this study, the cash crop ‘peanut’ was used as an example to explore vulnerability. Through the atmosphere–plant–soil continuum system, a single index that could represent different types of droughts affecting peanuts was selected and weighted using the CRITIC weighting method to construct a multi-source data fusion drought index (MFDI). Then, Pearson correlation analysis between the comprehensive drought index and relative meteorological yield and the Mann–Kendall trend test for different growth periods of peanuts were used to verify MFDI and analyze the variation over time. A three-dimensional vulnerability assessment method of drought intensity–drought duration–yield reduction rate was established based on the run theory and trend surface analysis. The results show that the constructed multi-source data fusion drought index (MFDI) can more accurately characterize the actual drought conditions of peanuts in Shandong Province. The MFDI results showed that the drought severity in the coastal areas of the study area decreased with the growth and development of peanuts, while the drought became more severe in the western and northern parts during the late growth period of peanuts. The vulnerability surface of the drought intensity–drought duration–yield reduction rate showed that when the drought intensity was &lt; 0.8 and the duration was &lt; 3.5 months, the vulnerability of peanut crops was low, and then with the increase in drought intensity or duration, the vulnerability increased. The impact of drought duration cannot be ignored. In contrast to traditional vulnerability assessment methods, this study established a three-dimensional vulnerability surface, which provides a new approach for agricultural drought vulnerability assessment. The research results are helpful for a deeper understanding of the relationship between drought and crop vulnerability and provide scientific support for local governments in formulating disaster prevention and mitigation policies.

Evaluating the climatic and socio-economic influences on the agricultural drought vulnerability in Jharkhand.

Environmental hazards like drought lead to degrading food production and adversely impact the agro-economy. This study investigates the contributions of different climatic and socio-economic variables to agricultural drought in Jharkhand. The three primary criteria, i.e., exposure (E), sensitivity (S), and adaptive capacity (AC), responsible for agricultural drought vulnerability, were examined to identify the drought-prone areas. Long-term (1958-2020) gridded climatic datasets obtained from the Terra-climate global dataset, MODIS vegetation index dataset (MOD13Q1) for the years 2001-2020, different soil parameters obtained from the ISRIC global soil database and state agricultural portal of Jharkhand, and different socio-economic datasets obtained from census data (2011) provided by Govt. of India, were utilized for this study. Analytic Hierarchy Process (AHP) was used to estimate the weighted contribution of the indicator variables falling under each criterion (E, S, and AC), and three criteria index maps were generated. These separate maps were further integrated to generate the final vulnerability index map. Finally, the study area was categorized into different zones based on the drought vulnerability index value ranging from 0 to 1, according to the severity of the drought. It was observed that about 4.05%, 28.12%, and 37.07% of the total geographical area is very highly, highly, and moderately vulnerable to agricultural drought, respectively. Amongst the three primary criteria, exposure showed a significant positive correlation (R = 0.61), and sensitivity showed a strong positive correlation (R = 0.55) with vulnerability. The adaptive capacity was negatively correlated (R = -0.75) with the vulnerability. However, putting equal weights to the variables to calculate the vulnerability, the exposure and sensitivity indicators showed a significant positive correlation with the vulnerability, with an R-value of 0.82 and 0.79, respectively. In contrast, the adaptive capacity showed a negative correlation with the vulnerability with R = -0.75.

Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation

The problem of drought in India is a major issue in terms of various adverse impacts on livelihood of society. Drought Early Warning System (DEWS), a real-time drought-monitoring tool, has reported that over a fifth of India's geographical area (21.06 %) is suffering drought-like situations. This is 62 % larger than the drought-affected area during the same period last year, which was 7.86 %. Drought affects 21.06 %, with conditions ranging from unusually dry to extremely dry. While 1.63 % and 1.73 % of the area are experiencing ‘extreme’ or ‘exceptional’ dry conditions, 2.17 % is experiencing ‘severe’ dry conditions. Under ‘moderate’ dry circumstances, up to 8.15 % is possible. In this perspective groundwater vulnerability assessment in the overall country is needed for implementing the sustainable and long-term strategies for escaping from this type of hazardous situation. The main objective of this study is to estimate the drought vulnerability in changing climate which eventually influences the food security of India. The groundwater overdraft is one of the crucial elements in agricultural drought vulnerability. Various related parameters have been selected for estimating the drought vulnerability and its impact to food security in India. Here, MaxEnt (maximum entropy) and ANN (analytical neural network) has been considered in this perspective. The AUC values for the training datasets in the ANN and MaxEnt model are 0.891 and 0.921, respectively. The AUC values in ANN and MaxEnt model for the validation datasets are 0.876 and 0.904, respectively. Here MaxEnt model is most optimal than ANN considering predictive accuracy. From this study analysis it is established that western, south and middle portion of country is very much prone to drought vulnerability. So, special emphases in terms of the regional planning have to be taken into consideration for sustainable planning.

Evaluation of Drought Vulnerability of Maize and Influencing Factors in Songliao Plain Based on the SE-DEA-Tobit Model

Rain-fed agriculture is easily affected by meteorological disasters, especially drought. As an important factor of risk formation, actively carrying out agricultural drought vulnerability assessments is conducive to improving food security and reducing economic losses. In this study, an SE-DEA model with regional exposure and drought risk as input factors and the maize yield reduction rate and drought-affected area as output factors is established. The aim is to evaluate and zone the drought vulnerability of the maize belt in the Songliao Plain. The results show the following: (1) From 2000 to 2019, the drought vulnerability of maize showed a fluctuating increasing trend. The vulnerability in Harbin and central Jilin Province is high, which is extremely unfavorable for maize production. (2) Comparing the historical disaster data with the drought vulnerability map generated using the SE-DEA model, it could be found that the results obtained using the SE-DEA model are reliable. (3) The Tobit model shows that the proportion of the effective irrigated area is more important to alleviate vulnerability. For drought vulnerability zoning using a cluster analysis, we suggest that regulated deficit irrigation should be actively developed in high-vulnerability areas to ensure maize yield while improving water efficiency. The results of this study can provide a basis for the development of drought mitigation and loss reduction strategies, and they provide new ideas for future research.

Evaluation of regional agricultural drought vulnerability based on unbiased generalized grey relational closeness degree

PurposeThe purpose of this study is to propose an unbiased generalized grey relational closeness evaluation model to improve the accuracy of regional agricultural drought vulnerability decision-making results, as well as to provide theoretical support for reducing agricultural drought risk and losses.Design/methodology/approachThe index weight is calculated using a rough set and deviation minimization criterion, and the relational degree between the research object and the double reference sequence is thoroughly investigated using the generalized grey relational closeness degree. Because different index rankings can correspond to different closeness degrees, the Monte Carlo method was used to calculate an unbiased estimate of the generalized grey relational closeness degree, which was used as a decision basis.FindingsAgricultural drought vulnerability in Henan Province in 2019 was clearly spatially differentiated. The vulnerability to agricultural drought in the southern and eastern regions was generally higher than that in other regions. The evaluation results of this model are highly stable and reliable compared to those of the traditional generalized grey relational evaluation model.Practical implicationsThis study proposes an evaluation model based on an unbiased generalized grey relational closeness degree, which is important to supplement the grey relational analysis method system and plays a positive role in promoting the quantitative evaluation of regional agricultural drought vulnerability.Originality/valueThe Monte Carlo method is used to calculate the unbiased estimation of the generalized grey relational closeness degree, which solves the problem of the replacement dependence of the traditional generalized grey relational degree and the one-sidedness of the evaluation results, and provides a new research idea for the evaluation of regional agricultural drought vulnerability under cross-sectional informatics.

Assessing agricultural and hydrological drought vulnerability in a savanna ecological zone of Sub-Saharan Africa

Assessing agricultural and hydrological drought vulnerability in a savanna ecological zone of Sub-Saharan Africa

Assessing Agricultural Drought Vulnerability in the Savanna Region of Nigeria

Assessing Agricultural Drought Vulnerability in the Savanna Region of Nigeria

Assessing the Vulnerability of Agricultural Systems to Drought in Kyrgyzstan

As climate change worsens, the frequent occurrence of extreme drought events will further threaten the agricultural systems of all countries in the world. Kyrgyzstan is a country with agriculture and animal husbandry as its main industries, with a weak industrial base, and agriculture plays an important role in the national economy. Kyrgyzstan is located in Central Asia and suffers from a dry climate and frequent droughts. Thus, an integral analysis of the vulnerability of Kyrgyzstan’s agricultural system is of great significance for this country’s socio-economic stability. In this study, we comprehensively analyze the agricultural system drought vulnerability of Kyrgyzstan from three dimensions of sensitivity, adaptability and exposure. The results show that the areas of higher vulnerability in Kyrgyzstan’s agricultural system are distributed in the eastern mountainous, northwest and southwest areas. In addition, regions with low vulnerability are mainly concentrated in the central area. Kyrgyzstan has abundant water resources, but the supporting infrastructure construction is relatively backward. The imperfect irrigation facilities have greatly restricted the development of agriculture and have also increased the vulnerability of the agricultural systems. In the face of climate change, the region may face more severe drought disasters, so increasing infrastructure investment and building a complete irrigation system and water use plan are the keys to reducing the vulnerability of Kyrgyzstan’s agricultural system.

Data-driven structure selection for the grey NGMC(1,N) model

PurposeWith the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.Design/methodology/approachThe linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.FindingsBy adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.Practical implicationsThis paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.Originality/valueBased on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

IDENTIFIKASI SEBARAN KERENTANAN KEKERINGAN PERTANIAN MENGGUNAKAN ANALYTICAL HIERARCHY PROCESS (AHP) DI KABUPATEN TEMANGGUNG

The decreased rainfall in Indonesia is mainly influenced by the east monsoon so air pressure from the southern hemisphere which is dry will flow through Indonesia. In a relatively long time, this may cause drought condition on agricultural land in Indonesia in general and in Temanggung Regency in particular. In addition, ENSO (El Nino Southern Oscillation) contributed to the decreased rainfall in Indonesia. This phenomenon will be more intensive and extreme with the existence of global warming. The identification of vulnerability of agricultural drought is an effort to mitigate disasters. This study aims to determine the distribution of agricultural drought and determine the factors that influence agricultural drought in Temangung Regency. The research method used the Analytical Hierarchy Process (AHP) to build a model of agricultural drought vulnerability by considering several factors. The results showed that the area of agricultural land which is vulnerable and very vulnerable to drought is 86,2 km2 and 74,14 km2, while agricultural land with moderate vulnerability is 208,21 km2, and agricultural land which is not vulnerable and very not vulnerable to drought is 128,15 km2 and 267,33 km2. The main factor as a determinant of agricultural drought in Temanggung Regency is rainfall. Meanwhile, the next factor is the respective land cover and soil texture. This research concludes that the effect of slope is not a big impact on agricultural drought in Temanggung Regency.

Identifying Influencing Factors of Regional Agricultural Drought Vulnerability Based on PSR-TGRC Method

Regional agricultural drought vulnerability (RADV) is a complex nonlinear problem caused by the interaction of multiple factors, and an objective and systematic method is proposed by this paper to identify its influencing factors, which plays an important role in preventing and regulating the risks of regional agricultural drought. Firstly, to provide a reference for the evaluation problem in selecting the number of factors, the influencing factors affecting RADV are revealed by using the method of phase space reconstruction (PSR). Secondly, to rank the importance of influencing factors, a grey trend relational analysis (TGRA) method is proposed, considering the dynamic development relationship between the RADV index and the influencing factors and integrating the absolute and relative variation of sequences in each corresponding period. Finally, to reduce the collinearity between the influencing factors, a grey trend relational clustering (TGRC) analysis method is proposed. According to the above steps, the process of identifying factors based on PSR-TGRC method is formed. Taking Henan Province as an example, 14 main influencing factors and their effects on RADV are identified from all 42 factors, and the identification results which are consistent with the actual drought relief work show the rationality and practicality of PSR-TGRC method and provide theoretical support for formulating strategies of regional agricultural disaster prevention and mitigation.

Agricultural drought vulnerability assessment and diagnosis based on entropy fuzzy pattern recognition and subtraction set pair potential

The assessment and diagnosis of agricultural drought vulnerability is the basis for the scientific control and prevention of agricultural drought risk. In order to quantitatively evaluate the regional agricultural drought vulnerability and effectively deal with the fuzziness and randomness between evaluation sample and evaluation grade, the entropy fuzzy pattern recognition model, which was coupled with maximum information entropy principle and fuzzy pattern recognition method, was applied to the evaluation of agricultural drought vulnerability. Furthermore, in order to find methods to reduce drought vulnerability, the subtraction set pair potential method was used to further identify the main influence factors of agricultural drought vulnerability. In addition, a case study was carried out in Bengbu City, China. The results showed that during the period from 2001 to 2010, the evaluation value of agricultural drought vulnerability in Bengbu dropped from grade IV to III, which meant that the tolerance of agricultural system to drought had been improved. Moreover, the main factors that affected the vulnerability in Bengbu were precipitation, water use efficiency and irrigation protection area rate. And the latter two were the main objects of regulation and control. Compared with the other three methods, the physical concept of drought vulnerability assessment model based on entropy fuzzy pattern recognition is more obvious, and the evaluation results are reasonable and credible. The drought vulnerability diagnosis method based on subtraction set pair potential can accurately identify key influence factor and provide measures to reduce drought vulnerability and improve drought risk management.

Assessment on Agricultural Drought Vulnerability and Spatial Heterogeneity Study in China.

Reducing drought vulnerability is a basis to achieve sustainable development in agriculture. The study focuses on agricultural drought vulnerability in China by selecting 12 indicators from two aspects: drought sensitivity and resilience to drought. In this study, the degree of agricultural drought vulnerability in China has been evaluated by entropy weight method and weighted comprehensive scoring method. The influencing factors have also been analyzed by a contribution model. The results show that: (1) From 1978 to 2018, agricultural drought vulnerability showed a decreasing trend in China with more less vulnerable to mildly vulnerable cities, and less highly vulnerable cities. At the same time, there is a trend where highly vulnerable cities have been converted to mildly vulnerable cities, whereas mildly vulnerable cities have been converted to less vulnerable cities. (2) This paper analyzes the influencing factors of agricultural drought vulnerability by dividing China into six geographic regions. It reveals that the contribution rate of resilience index is over 50% in the central, southern, and eastern parts of China, where agricultural drought vulnerability is relatively low. However, the contribution rate of sensitivity is 75% in the Southwest and Northwest region, where the agricultural drought vulnerability is relatively high. Among influencing factors, the multiple-crop index, the proportion of the rural population and the forest coverage rate have higher contribution rate. This study carries reference significance for understanding the vulnerability of agricultural drought in China and it provides measures for drought prevention and mitigation.