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The objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda.

This study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model.

It is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields.

The primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions.

This is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.

This paper aims to inform the readers an overview of expected impacts of sea level rise (SLR) and climate change on rice crops area, yield and the urgent need to build climate responsive infrastructures to a coastal district, Alappuzha – a high-risk area which is already under mean sea level (MSL). This research carried out to understand the realities and impacts with respect to the exposures of rise in SLR and possible inundation extent of crop land. The extreme precipitation events have caused crop loss and damage, numerous casualties and enormous economic loss in this district during the recent past and project the likely impacts under anticipated climate change.

Global sea levels have already been risen noticeably as a result of climate change, and this trend is anticipated to continue. To reflect on the research objectives, the paper projects a climate change scenario analysis and impact assessment on the major crop grown, i.e. rice, using a crop simulation model, DSSAT 4.7 as the first part of the study. QGIS 3.28 version and Erdas Imagine software were used for land use land cover analysis and to delineate possible inundation in the major land use land cover, especially in agriculture area under SLR scenario. It points out the need to equip the district urgently with climate responsive agriculture strategies as majority of the area comes under 10 mts of elevation as per the Sentinel 2 data. For better adapting to the current and future climate change impacts in the aspects of built environment such as early warnings in farm sector in particular and forests, urban water management, transportation systems, building construction and operation and land use planning in general. Climate change is no longer a policy issue alone; now it is a common man’s nightmare. For a coastal state like Kerala, extreme climate events during 2018 and 2019 and 2021 have posed substantial impacts and damages on the environment and society. The impacts hit the vulnerable communities in multiple ways.

From the analysis, it was revealed that there is an increasing trend in rainfall observed over the past three decades in Alappuzha district. It is projected that day and night time temperatures may increase in Alappuzha by 2.5°C and 2.6°C by 2100, respectively, under RCP 4.5. With unchecked pollution or emission reduction actions, warming may further rise and hence the median projection when SLR reaches 2.4 meters (8 ft) at Alappuzha to Cochin coast is 2130s. The possible inundation analysis shows that around 53.48% of the coastal agriculture land may be likely inundated if SLR is only with mitigation measures such as extreme carbon cuts, SLR rise can be delayed till 2200. Alappuzha is known as the rice bowl of Kerala; however, it is highly exposed to climate vulnerability in terms of its unique environmental geographical settings like coastal wetlands, lagoons and sand beaches. DSSAT simulations shows that Uma rice, a major ruling variety in the region, may have yield reductions of up to 13% in the near century for Alappuzha.

This paper in general explains the projected climate change perspectives for Alappuzha, a climate change hotspot of Kerala with respect to SLR and coastal agriculture. and a review of the progression of DRR in the built environment and mainstreaming CCA and DRR by government and other agencies in the state.

This study underscores the urgent need for climate-responsive agricultural strategies in Alappuzha, Kerala, due to anticipated sea level rise, climate change, and land use changes. Equipping farmers with the knowledge and tools to adapt is essential for ensuring food security and sustainable livelihoods. Implementing climate-resilient practices and technologies will help mitigate adverse effects on rice crops, promoting economic stability and resilience in the region. Involving local stakeholders in the adaptation process is crucial, as their participation can enhance collaboration, increase awareness, and accelerate the adoption of sustainable agricultural practices, making the transition smoother and more effective.

It is the responsibility of the scientific community to inform the knowledge gained for the benefit of the society, especially on criticality of altering the existing land use pattern and building climate resilient coastal infrastructures. Studies such as this can stand as basis for implementing planned adaption actions. This is to conclude that instead of working in silos, mainstreaming climate change adaptation holistically across sectors is very necessary at this crucial hour. Participatory action plans and policies involving all local stakeholders can strengthen awareness and fasten the learning processes for adaptation including managed retreats.

At present, there are no specific studies, on the impacts of climate change and SLR on rice cropping systems in the district which specifically inform how to mainstream adaptation in the agriculture strategies in low lying coastal zones of Alappuzha.

This study aims to identify seasonal drought using standardized precipitation index (SPI). The following specific objectives are to generate result and identify seasonal drought and determine different scale of seasonal drought and its impacts on cropping season.

Seasonal SPI was calculated using long-term rainfall data for three seasons. The SPI was calculated using the formula and it is effective for the determinants. This study showed the functional relationship between drought duration, frequency and drought time scale using the SPI. SPI=X−X¯σ.

Seasonal drought occurs more frequently in Bangladesh that affects crops and the agricultural economy every year. More severe drought was recorded during the Kharif-1 and Kharif-2 seasons and most crops were affected in these two seasons. No severe or moderate drought was recorded during the Rabi season. The results showed that monsoon crops were severely affected severely by extreme and severe droughts during the Kharif-2 season. Eventually, the people remain jobless during the monsoon, and they experience food shortages like monga. Several obstacles were recorded during the season, including delayed preparation of land, sowing, transplanting and other farming activities because of monsoon droughts. This study revealed that very frequently, mild dryness occurs in winter, but crop loss is minimal. The scale and occurrence of extreme droughts are more frequent during monsoons and reduce crop yields, affecting livelihoods in the study area. Seasonal drought affects cropping patterns as well as reduce crop yields.

The outcome of this study derived from the secondary data and field data.

This study aims to understand how climatic drivers of change will affect rural communities living in the hot semiarid region of Bhokardan Taluka of Jalna district in the Indian state of Maharashtra. In the context of the economic and social change they are experiencing, the concern is to evolve ways that enable them to cope with, adapt to and benefit from these challenges.

The focus of most of the climate change studies is on the short- to long-term trends of weather parameters such as rainfall, temperature and extreme weather events. The impact of climate variability and changing patterns on the local communities, the local economy, livelihoods and social life in specific geographies is less explored.

As the impacts of climatic and nonclimatic drivers of change are cross-sectoral, diverse, multidimensional, interlinked and dynamic, this study has adopted a transdisciplinary “research-in-use” approach involving multidisciplinary teams covering the aspects such as changes in land use and land cover, surface and groundwater status, edaphic conditions, crops and livestock, climate analysis including projected changes, socioeconomic analysis, people’s experience of climate variability and their current coping strategies and resilience (vulnerability) analysis of communities and various livelihood groups.

The study was based on the peoples’ perspective and recommendation based on the local communities ability to cope up with climate change. However, a statistical analysis perspective is missing in the present study.

Based on these findings, a set of implementation-focused recommendations are made that are aimed at conserving and enhancing the resilience of the foundations that uphold and sustain the social and economic well-being of the rural communities in Bhokardan taluka, namely, land, water, agriculture, livestock, food and nutrition security, livelihoods, market access and social capital.

This study investigates the impact of board funds, banking credit, and economic development on food production in the context of South Asian economies (India, Pakistan, Bangladesh, Sri Lanka, and Nepal).

This study used data from the World Development Indicators covering the years 1991–2019. To investigate the relationship between the variables of the study, we employed the panel unit root test, panel cointegration test, cross-sectional dependence test, fully modified least squares (FMOLS), and panel dynamic least squares (DOLS) estimators.

The empirical results indicate that board funding significantly increase food production; however, banking credit had a negative impact. Furthermore, the findings indicate that economic development, Arable land, fertilizer consumption, and agricultural employment play a leading role in enhancing food production. The results of the Dumitrescu-Hurlin causality test also show substantiated the significance of the causal relationship among all variables.

South Asian countries should prioritize board funding, bank credit, and economic development in their long-term strategies. Ensuring financial access for farmers through micro-credit and public bank initiatives can spur agricultural productivity and economic growth.

This study is the first to combine board funding, banking credit, and economic development to better comprehend their potential impact on food production. Instead of using traditional approaches, this study focuses on these financial and developmental aspects as critical determinants for increasing food production, using evidence from South Asia.

The purpose of this paper is to assess the adoption of climate-smart agriculture (CSA) and its implication on improving the farming household food security status, their resilience and livelihood risk management of farmers.

This systematic review has followed procedures to accomplish the review, including literature searches, screening studies, data extraction, synthesis and presentation of the data.

Based on the result of the review, the determinants of CSA adoption can be categorized into five categories, including demographic factors (age, sex, family size, dependency ratio, education), economic factors (land size, household income, livestock ownership), institutional factors (extension services, training access, credit services, farm input, market distance), environmental factors (agroecology, change in precipitation, slope of land) and social factors (cooperatives membership, farmers perception). The result also shows that applying CSA practices has an indispensable role on increasing productivity, food security, income, building resilient livelihoods, minimizing production risk and alleviating poverty. This concluded CSA practice has a multidimensional role in the livelihood of agrarian population like Ethiopia, yet its adoption was constrained by several factors.

This review mainly emphasizes on the most commonly practiced CSA strategies that are examined by different scholars.

The objective of the research is to evaluate the carbon footprint of the green asparagus (Asparagus officinalis L.) supply chain, encompassing the agricultural production to the packaging stage in Italy, as it is the sixth largest producer and the second largest in Europe. It provides an assessment in the province of Foggia and highlights the global perspective of the carbon footprint application in agro-food systems.

The carbon footprint (ISO 14067:2018) considers 1 t of packaged fresh asparagus as a functional unit in the agricultural production and packaging stage and is based on primary data collected in one of the leading companies of asparagus production in the province of Foggia, which markets about 0.21 kt of asparagus per year produced in about 31 ha. Data were integrated with face-to-face in-depth interviews and pre-filled checklists.

Findings show that the carbon footprint of 1 t of packaged fresh asparagus is equivalent to 335.31 kgCO2eq, of which 61% in the agricultural stage and 39% in the packaging one. The majority of the emissions are associated with the fertigation and the diesel consumption for the transportation of workers. Farmers should adopt green electricity so as to reduce the emissions associated with the electric pump for the extraction of water from artesian wells. Moreover, it would be desirable to replace mineral urea phosphate with organic fertilizers.

To the best of the authors’ knowledge, scholars have not yet investigated the environmental impacts of the green asparagus supply chain, even if it represents one of the most cultivated vegetables worldwide, with a global production that amounts to 8.5 Mt per year.

This study aims to assess the effects of natural disasters on food security in a sample of 40 sub-Saharan African countries. First, the authors assess the effects of natural disasters on the four dimensions of food security and second, the authors disaggregate natural disaster using the two dimensions that are most representative, namely, hydrological and biological disasters.

The regressions are based on the generalised method of moments on a data set covering the period 2005–2020. Natural disasters are measured by the total number of people affected and food security by its characteristics: access, availability, use and sustainability.

The results show that natural disasters increase the prevalence of undernourishment but reduce dependence on cereal imports. An increase in natural disasters by 1% increases the prevalence of undernourishment by the same proportion. As for import dependency, a 1% increase in natural disasters reduces dependency by 2.2%. The disaggregated effects show that hydrological disasters are more significant than biological disasters in impacting food security. Floods reduce the average energy supply adequacy but also dependence on cereal imports. Policy implications are discussed.

The study complements the extant literature by assessing the effects of natural disasters on food security in a region where food insecurity is one of the worst in the world.

Long-term forecasts about commodity market indicators play an important role in informing policy and investment decisions by governments and market participants. Our study examines whether the accuracy of the multi-step forecasts can be improved using deep learning methods.

We first formulate a supervised learning problem and set benchmarks for forecast accuracy using traditional econometric models. We then train a set of deep neural networks and measure their performance against the benchmark.

We find that while the United States Department of Agriculture (USDA) baseline projections perform better for shorter forecast horizons, the performance of the deep neural networks improves for longer horizons. The findings may inform future revisions of the forecasting process.

This study demonstrates an application of deep learning methods to multi-horizon forecasts of agri-cultural commodities, which is a departure from the current methods used in producing these types of forecasts.

This study has empirically investigated the impacts of climate change on agricultural productivity worldwide, considering the ranking of agriculture productivity. Additionally, the study has estimated the extent to which climate change favoured agriculture productivity from a global perspective.

The study prepared a suitable econometric model and employed the quantile panel Autoregressive Distributed Lag technique with a two-step Error Correction Mechanism to assess the influence of global warming on worldwide agrarian productivity.

The estimated results provide evidence for the nonlinear impacts of climate change on agriculture productivity across all quantiles. Moreover, threshold levels of average annual temperature rise with the improvement of agricultural productivity, depicting that low-productive areas are highly vulnerable to global warming. Additionally, agricultural inputs like labour, capital and irrigated land are positively related to agricultural productivity, with relatively substantial marginal productivity in highly productive regions. Nevertheless, technological innovations are found to be more productive in low-productive areas.

Policymakers should prioritize region-specific climate-smart agriculture by targeting policies to increase agricultural productivity and minimize the effects of climate change on food security and nutrition.

Despite significant research in this area, there remains a knowledge gap on the nature of this relationship, especially regarding productivity thresholds under warming. The study aims to fill this gap, offering valuable insights to guide policy actions and adaptation strategies to mitigate the adverse impacts of climate change on agriculture productivity.