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The purpose of this paper is to assess the agricultural practices in a drought-prone region of India in an effort to find out how science, technology and innovation (STI) measures can address the existing problems and help achieve sustainable solutions. This study has been planned with two specific objectives: to study the agricultural practices of small and marginal-holding farmers in a drought-prone region and to examine the opportunities for suitable interventions to mitigate the impacts of droughts. The study is based on primary survey conducted in Banda district of Bundelkhand region, Uttar Pradesh, India.

Empirical survey was done in eight different blocks of a drought-prone region of India using structured questionnaire. The questionnaire was pre-tested with a group of 12 farmers during a workshop through a pilot survey conducted during April 2017. Stratified sampling based on land holdings (small farmers having 1–2 ha of land, medium farmers having 2.1–5 ha of land and large farmers having more than 5 ha of land) and irrigation types (canals and tube wells) were utilised in different blocks of the district for selecting farmers in the surveyed villages.

Findings suggest that due to various reasons like change in climatic conditions, frequent crop failure, crop diseases and high cost of production, farmers have adopted certain crops which are not suited to their agro-climatic conditions. The paper recommends that farmer’s school or “on-farm training school” have to be initiated to integrate farmers’ traditional knowledge with modern knowledge systems with amalgamation of STI tools.

Uttar Pradesh is divided into nine agro-climatic zones; however, this study is focused on Bundelkhand and may be region specific, though the findings are important for other drought-prone areas.

The paper links the existing agricultural practices and further linking them with farmers’ socio-economic, cultural and environmental settings. Only 17.5 per cent of respondents owned any agricultural equipment due to high cost of farm tools, difficulty in taking equipements on rental basis and lack of sharing tools among the farmers.

This paper targets small and marginal farmers in the drought-prone region of India who face the dual shock of climate impacts and poverty. Adoption of modern agricultural practices and use of technology is inadequate which is further hampered by ignorance of such practices, high costs and impracticality in the case of small land holdings.

This paper has advocated for well-organised, efficient and result-oriented STI system to mitigate the adverse impacts of drought-prone agriculture. Farming community in drough-prone areas needs adequate investment, local-specific technology, better quality inputs, real-time information on weather and most importantly latest know-how for sustaining commercial and cost effective sustainable agriculture.

The purpose of this paper is to examine whether Australian agricultural firms display big bath behaviour during droughts by recognising extraordinary and abnormal losses. It is hypothesised that Australian agricultural firms are more likely to report big bath losses in drought years than in non‐drought years and, in a given drought year, agricultural firms are more likely to report big bath losses than firms in other industries.

The authors analyse 405 firm‐years data for agricultural firms over 1980‐1995. For comparison, they also analyse matched‐pair samples of 17 and 30 non‐agricultural firms for the drought years of 1983 and 1995, and matched‐pair samples of 19 non‐agricultural firms for the non‐drought years of 1986 and 1990, respectively. Both univariate and multivariate analyses are used to test the hypotheses.

It is found that agricultural firms are more likely to take big baths in drought years than in non‐drought years. Further, in a given drought year, agricultural firms are more likely to take big baths than non‐agricultural firms. Further analyses of sales, profitability, and extraordinary and abnormal items support the idea that big baths reflect managerial opportunism rather than the economic consequences of droughts.

Previous studies have not investigated the impact of natural calamities like flood and drought on accounting choices. This paper makes an original contribution to the accounting literature by documenting evidence on the extent to which an act of nature, over which management has little or no control, can influence accounting choices.

A core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.

A two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.

The results exemplified by China's Henan Province in the years 2008–2016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.

The proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.

The purpose of this paper is to establish a grey cloud incidence clustering model to assess the drought disaster degree under 15 indexes in 18 cities of Henan province.

The grey incidence degree between each index and ideal index is used to determine the index weight and combined with the subjective weight, the comprehensive weight is given; the traditional possibility function is transformed into grey cloud possibility function by using the principle of maximum deviation and maximum entropy, which fully reflects the coexistence of multiple decision-making conclusions and constructs the grey cloud incidence clustering model.

The drought disaster degree of Henan province is divided into four grades under the selected 15 indexes. The drought grades show obvious regional differences. The risk levels of the east and southwest are higher, and the risk levels of the north and southeast are lower. This result is consistent with the study of drought disaster grades in Henan province, which shows the practicability and usefulness of the model.

It provides an effective method for the assessment of drought disaster grade and the basis for formulating disaster prevention and mitigation plan.

By studying the method of multiattribute and multistage decision-making with interval grey number information. The objective weight model of index value is designed, and the subjective weight is given by experts. On the basis of the two, the comprehensive weight of subjective and objective combination is proposed, which effectively weakens the randomness of subjective weight and reasonably reflects the practicality of index decision-making. The time weight reflects the dynamic of the index. The traditional possibility function is transformed into the grey cloud possibility function, which effectively takes advantage of the grey cloud model in dealing with the coexistence of fuzzy information, grey information and random information. Thus, the conflict between the decision-making results and the objective reality is effectively solved. The interval grey number can make full use of the effective information and improve the accuracy of the actual information.

The Monsoon Asian region has a much wider rainfall distribution than other regions of the world. The countries in this region are characterized mostly by floods and typhoons, which result from the interplay among the ocean, the atmosphere, and the land. Thus, many factors affect the strength of the rainfall, including sea surface temperatures in the Indian and Pacific Oceans, variations in solar output, land snow cover and soil moisture over the Asian continent, and the position and strength of prevailing winds. The links between these factors and monsoons appear to wax and wane over time, and the observational record is too short to explain this longer-term variability. Precipitation and surface wind maps of Asia during the summer months of June to August show the average spatial patterns of monsoon circulation and moisture.

Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years.

The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal.

The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone.

Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe.

Of all the natural disasters, drought is the most gradual and the most hard to predict. However, this insidious disaster continually affects the lives and livelihoods of farmers living in drought-affected areas. The northwestern part of Bangladesh is recognized as being more severely affected by drought than the rest of the country, as drought is a recurring event in this area. It has substantial impacts on agriculture and causes great suffering for farmers – in particular, poor and small farmers, who are more vulnerable to drought. Therefore, this study tries to illustrate farmers’ existing coping practices with regard to drought. It also addresses their prioritized adaptation practices, which are based on local context and available resources. This study not only focuses on the implementation of these adaptation practices from the national to the local level, but it also mentions various roles of stakeholders and a definite timeframe for each adaptation practice.

With 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.

The 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.

By 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.

This 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.

Based 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.

The purpose of this paper is to investigate how a drought which initially affects agricultural productivity can ultimately affect an entire economy. The study aims to assess the magnitude of the impact as well as highlight key issues that can inform the implementation of drought mitigation programmes.

The paper presents the literature on the economic impact of drought and uses a computable general equilibrium model where productivity shocks are applied to the agricultural industries following which the resulting impacts on the rest of the sectors of the economy are obtained.

The findings show that the key macroeconomic variables, namely, real GDP, industry output, employment, the trade balance and household consumption are negatively affected by the drought shock.

The results point to the fact that in the absence of drought mitigation mechanisms, the occurrence of even a short drought as modelled in this paper can impose substantial socioeconomic losses.

First, a general equilibrium framework which uses climate and economic data when evaluating the social-economic impacts of drought is used. Most studies employ partial equilibrium analysis in analysing drought impacts on specific sectors or crops within a limited geographical area. Others use global or multi-regional models which impose averages on the observed impacts. The current study provides valuable insights on the potential damage which droughts can impose on a single economy. This gives a basis for decision making to support drought mitigation policies and programmes.

The purpose of this paper is to provide useful information for members of the weather community and policy makers, to help them understand the full scope of drought economic impacts and assessment methodologies, and to help determine the feasibility of future drought mitigation programs.

To accomplish the objective, the paper reviews the literature of drought economic impact studies in both agricultural and non‐agricultural sectors, summarizes the methods and data employed, compares the various results, and investigates the problems and limitations of previous studies.

The paper concludes with a discussion of the challenges and directions of future improvement on drought economic impact assessment.

This paper gives a comprehensive review of drought economic impacts and the associated quantitative assessment methodologies, which provides valuable information to rational decisions supporting drought mitigation policies and programs.