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The 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.

Firstly, 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.

The 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.

The 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.

By 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.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

As an important measure to promote sustainable development, green finance has developed rapidly in recent years. In order to comprehensively analyze the positive and negative indicators of the influencing factors of green finance, this paper puts forward a grey relational method of spatial-temporal panel data from the perspective of the development trend of the object dimension indicators and the performance difference between the time dimension indicators.

From the different perspectives of object dimension and time dimension, the positive and negative indicators are standardized differently considering the reverse of indicators and characterizing factors. The grey absolute relational degree is used to define the matrix sequence. This method reflects the development trend of objects in time and the difference characteristics among objects, which comprehensively represents the correlation between the reference panel and the comparison panel.

The results show that: (1) The object dimension reflects the internal driving force of the development of green finance in each provincial administrative region and the time dimension reflects the relationship between regional differences of influencing factors and green finance. (2) From the object dimension, the influencing factors of green finance from high to low are economic development potential, economic development level, air temperature, policy support, green innovation and air quality. (3) From the time dimension, the influencing factors of green finance from high to low are green innovation, air quality, economic development potential, economic development level, policy support and air temperature.

The different standardized processing methods of positive and negative indicators proposed in this paper not only eliminate the sample dimension, but also study the grey relational degree among the indicator panels from different reference dimensions. The proposed model is applied to identify the influencing factors of green finance, which expands the practical application scope of the grey relational model. The research results can provide reference for relevant departments to better promote the development of green finance.

This paper aims to document indigenous knowledge systems (IKS) used for short- and long-range rainfall prediction by small holder farmers in three communities of Guruve District, in north-eastern Zimbabwe. The study also investigated farmers’ perceptions of contemporary forecasts and the reliability of both IKS and contemporary forecasts.

Data were collected among small holder farmers in Guruve District using household interviews and focus group discussions in three wards in the district, grouped according to their agro-climate into high and low rainfall areas. To get an expert view of the issues, key informant interviews were held with key agricultural extension personnel and traditional leaders.

Results obtained showed show high dependence on IKS-based forecasts in the district. Over 80 per cent of the farmers used at least one form of IKS for short- and long-range forecasting, as they are easily understood and applicable to their local situations. Tree phenology, migration and behaviour of some bird species and insects, and observation of atmospheric phenomena were the common indicators used. Tree phenology was the most common with over 80 per cent of farmers using this indicator. While some respondents (60 per cent) viewed forecasts derived from IKS as more reliable than science-based forecasts, 69 per cent preferred an integration of the two methods.

The simplicity and location specificity of IKS-based forecasts makes them potentially useful to smallholder farmers, climate scientists and policymakers in tracking change in these areas for more effective climate change response strategies and policymaking.

Communities around the world are already vulnerable to disasters, the “Global Assessment Report: Disaster Risk Reduction” presents compelling new evidence of concentration of risk in many developing countries. The “Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report” reports that climate change is expected to be accompanied by an increased frequency and intensity of extreme climate events in many parts of the world. Moreover, climate change will aggravate many of the socioeconomic factors that drive vulnerability.

Agriculture insurance is the panacea for the farming community. Many policy interventions were implemented for stimulating agriculture insurance access to farmers in India. However, access to agriculture insurance constantly remained one of the major challenges to Indian policy planners. The goal of the present paper is to explore current policy interventions in the area of agriculture insurance in India.

The present paper reviews and analyzes the evidence literature through a content analysis method on development and performance analysis perspective of existing agriculture insurance schemes in India.

Agriculture insurance is a significant risk management policy, but this is not easily reachable to the majority of farmers in India. The government of India introduces a novel agriculture scheme every decade, but every crop insurance scheme was inconsistent and ineffective owing to operational defects. Agriculture insurance in India is still developing in terms of coverage, scope, and exposure, but farmers' dissatisfaction about agriculture insurance turned out to be a negative word of mouth. Insurance illiteracy and farmers' preference for agriculture relief payments are the main reasons for limited access to agriculture insurance. The current crop insurance schemes are improperly operated because of implementation issues at the state level.

This paper will be useful for researchers and academicians to analyze the past and present status of crop insurance in India.

The paper is the unique work of the authors as it has attempted to present India's journey with agriculture insurance. An effort is made in the present study to provide a comprehensive and holistic developmental and performance analysis perspective of agriculture insurance in India.

History has taught us that every aspect of the world around us is changing. Right from its formation, the earth has been evolving climatically, edaphically, and biotically to its present state. The forcing for all these changes in the past was natural, and human activities had least influence till the industrial revolution. Since the beginning of the 18th century, human activities associated with the industrial revolution have changed the composition of the atmosphere and thereby having a greater influence on the earth's climate. The use of fossil fuels like coal and oil coupled with deforestation has increased the concentration of heat-trapping “greenhouse gases,” which prevent the heat from the earth escaping to space. Because of this, the very greenhouse gases, which helped sustain life on the earth under normal circumstances, have become detrimental due to its higher concentration. Several models have predicted that the rising concentrations of greenhouse gases produce an increase in the average surface temperature of the earth over time. Rising temperatures may, in turn, produce changes in precipitation patterns, storm severity, and sea level, commonly referred to as “climate change.” The Intergovernmental Panel on Climate Change (IPCC) defines climate change broadly as “any change in climate over time whether due to natural variability or as a result of human activity.” The United Nations Framework Convention on Climate Change (UNFCCC) defines climate change as “a change of climate that is attributed directly or indirectly to human activity, that alters the composition of the global atmosphere, and that is in addition to natural climate variability over comparable time periods.”

Sri Lanka, as an island located near the southern tip of India and the Asian Continent and in the core area of the South Asian Monsoon has developed its own unique forms of Hydraulic Civilization. Sri Lanka is covered with a network of thousands of man-made lakes and ponds, known locally as “tanks,” numbering more than 25,000. Some are in the functional mode and others still remaining abandoned type. Many are thousands of years old and almost all show a high degree of sophistication in their construction and design. Sri Lanka's tanks are fascinatingly distributed in the cascades of tanks one below the other conserving water and soil and most effectively, acting as buffers against droughts while giving due consideration to maintaining the ecosystem equilibrium.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.