Assessment of Agricultural Drought Vulnerability in Tamil Nadu Using MODIS NDVI, NDWI and VSDI

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.

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Analysing agricultural drought vulnerability at sub-district level through exposure, sensitivity and adaptive capacity based composite index

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-65-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 65-70 ◽

Cited By ~ 3

Author(s):

C. S. Murthy ◽

B. Laxman ◽

M. V. R. Sesha Sai ◽

P. G. Diwakar

Keyword(s):

Adaptive Capacity ◽

Composite Index ◽

Vulnerability Index ◽

The State ◽

District Level ◽

Agricultural Drought ◽

Current Status ◽

Drought Vulnerability ◽

Related Data ◽

Land Holdings

Information on agricultural drought vulnerability status of different regions is extremely useful for implementation of long term drought management measures. A quantitative approach for measuring agricultural drought vulnerability at sub-district level was developed and implemented in the current study, which was carried-out in Andhra Pradesh state, India with the data of main cropping season i.e., kharif. The contributing indicators represent exposure, sensitivity and adaptive capacity components of vulnerability and were drawn from weather, soil, crop, irrigation and land holdings related data. After performing data normalisation and variance based weights generation, component wise composite indices were generated. Agricultural Drought Vulnerability Index (ADVI) was generated using the three component indices and beta distribution was fitted to it. Mandals (sub-district level administrative units) of the state were categorised into 5 classes – Less vulnerable, Moderately vulnerable, Vulnerable, Highly vulnerable and Very highly vulnerable. Districts dominant with vulnerable Mandals showed considerably larger variability of detrended yields of principal crops compared to the other districts, thus validating the index based vulnerability status. Current status of agricultural drought vulnerability in the state, based on ADVI, indicated that vulnerable to very highly vulnerable group of Mandals represent 54 % of total Mandals and about 55 % of the agricultural area and 65 % of the rainfed crop area. The variability in the agricultural drought vulnerability at disaggregated level was effectively captured by ADVI. The vulnerability status map is useful for diagnostic analysis and for formulating vulnerability reduction plans.

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Assessment of Agricultural Drought Risk in the Lancang-Mekong Region, South East Asia

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17176153 ◽

2020 ◽

Vol 17 (17) ◽

pp. 6153

Author(s):

Lei Zhang ◽

Wei Song ◽

Wen Song

Keyword(s):

Agricultural Production ◽

Agricultural Drought ◽

Mekong River ◽

Drought Risk ◽

Crop Water Stress Index ◽

Drought Vulnerability ◽

Mekong Region ◽

Drought Hazard ◽

South Central ◽

High Hazard

Natural disasters worldwide regularly impact on human activities. As a frequently occurring natural disaster, drought has adverse impacts on agricultural production. The Lancang-Mekong River is a transnational river running through China and five Southeast Asian countries and it is a vital water resource for irrigation in the region. Drought in the Lancang-Mekong Region (LMR) has occurred frequently in recent years. Assessing the risk of drought in the region is essential for rational planning of agricultural production and formulation of drought relief measures. In this study, an assessment of drought risk has been achieved by combining the hazard and vulnerability assessments for drought. The assessment of the drought hazard depends mainly on the standardized precipitation index (SPI). The assessment of drought vulnerability takes into account various indicators such as climatic factors (e.g., crop water stress index), soil factors (e.g., available water capacity), and irrigation factors (e.g., irrigation support). The results reveal that: (1) Drought distribution in the LMR is characterized by a spreading of the drought to countries along the middle and lower reaches of the Mekong River. Countries located in the middle and lower reaches of the Mekong River are more prone to drought. Laos, Thailand, and Cambodia are the regions with higher and high-drought risk levels. (2) The spatial distributions for the drought hazard and the drought vulnerability in the LMR exhibit significant differences as evidenced in the mapping results. High-hazard and high-vulnerability areas are mainly distributed in the middle LMR, and the middle to higher hazard areas and the middle to higher vulnerability areas are mainly distributed in the south-central LMR, while the low-hazard areas and the low-vulnerability areas are mainly in the north. (3) The majority of planting areas for sugarcane, rice, and cassava are located in the high-hazard areas. The distributions of drought-prone and high-hazard areas also correspond to the main agricultural areas in the LMR.

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Identification of Agricultural Drought Vulnerable Areas of Tamil Nadu, India – Using GIS Based Multi Criteria Analysis

Asian Journal of Environment and Disaster Management (AJEDM) – Focusing on Pro-active Risk Reduction in Asia ◽

10.3850/s17939240200900009x ◽

2009 ◽

Vol 01 (01) ◽

pp. 43 ◽

Cited By ~ 5

Author(s):

K. Chandrasekar ◽

M. V. R. Sesha Sai ◽

P. S. Roy ◽

V. Jayaraman ◽

R. R. Krishnamurthy

Keyword(s):

Tamil Nadu ◽

Agricultural Drought ◽

Multi Criteria Analysis

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

Grey Systems Theory and Application ◽

10.1108/gs-03-2021-0039 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Dang Luo ◽

Decai Sun

Keyword(s):

Correction Term ◽

Geometric Interpretation ◽

Selection Method ◽

Agricultural Drought ◽

Grey Model ◽

Model Parameters ◽

Drought Vulnerability ◽

Content Type ◽

Structure Selection ◽

Lasso Method

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.

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