Satellite-Based Meteorological and Agricultural Drought Monitoring for Agricultural Sustainability in Sri Lanka

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

Food Insecurity and Perceived Effects of COVID-19 on Livelihoods in Rural Sri Lanka

BACKGROUND

Little is known about how the COVID-19 pandemic has affected food security and livelihoods in Sri Lanka.

OBJECTIVE

This article aims to assess food insecurity, perceived effects of COVID-19, and coping mechanisms among agriculture-based households in rural Sri Lanka.

METHODS

We used 2 rounds of panel data from phone surveys (n = 1057 households) conducted in 5 districts. Food insecurity (30-day recall), perceived impacts of COVID-19 (6-month recall), and coping mechanisms (6-month recall) were assessed using a household questionnaire. To assess food insecurity, we used the 8-item Food Insecurity Experience Scale. We tested for differences between T1 (baseline: December 2020-February 2021) and T2 (follow-up: July 2021-September 2021) and explored the association between food insecurity and the perceived effect of COVID-19 on income using a logistic regression model.

RESULTS

Food insecurity was highly prevalent (T1: 75%, T2: 80%) but varied across districts. Most respondents were affected by COVID-19 and/or COVID-19-associated mitigation measures (T1: 84%, T2: 89%). Among affected households, commonly reported impacts included those on income (T1: 77%, T2: 76%), food costs (T1: 84%, T2: 83%), and travel (∼90% in both rounds). Agricultural activities were also adversely affected (T1: 64%, T2: 69%). About half of COVID-19-affected households reported selling livestock or assets to meet basic needs. Households whose income was impacted by COVID-19 were more likely to be food insecure (adjusted odds ratio: 2.56, P < .001).

CONCLUSIONS

Households in rural Sri Lanka experienced food insecurity and livelihood disturbances during the COVID-19 pandemic. Additional surveys are needed to assess recovery post-COVID-19 and to understand if programs that support livelihoods have been protective.

Harnessing Insights from Indicators-Based Resilience Assessment for Enhancing Sustainability in the Gurage Socio-Ecological Production Landscape of Ethiopia

Even though the mosaic of different land-use/land-cover types has long contributed to the resilience of socio-ecological production landscapes and seascapes in Ethiopia, recent data indicate that their sustainability is under threat. This study aims to evaluate landscape resilience by adopting a set of indicators for enhancing sustainability in the Gurage socio-ecological production landscape in Ethiopia. The authors employed a toolkit of indicators in the production landscape through a community-based scoring approach (1-5 Likert scale). The information from household surveys, land-use/land-cover analysis, and satellite-based drought incidents assessment was integrated with the ranking analysis to support the evaluations. The results revealed that landscape diversity, ecosystem protection, local governance, and social equity indicators had the highest landscape resilience ranks. In contrast, lower ranks are associated with knowledge, innovation, livelihoods, and well-being indicators. The overall resilience of the Gurage socio-ecological production landscape was estimated to be below average. Thus, strategies that enhance the resilience and sustainability of this socio-ecological landscape are essential. The findings could help draw the attention of policymakers and natural resource managers to building and strengthening the resilience of the landscape. This study demonstrates that indicators could aid in evaluating landscape resilience status along with other ancillary information, particularly in data-sparse regions. Methods of assessing resilience must be creative in such regions, and this paper may inform such efforts. In addition, the study recommends that landscape resilience indicators be improved by reducing subjective matter and including spatial-explicit dimensions for evaluating resilience.

Food availability and food selectivity of Sri Lanka Grey Hornbill Ocyceros gingalensis Shaw, 1811 in Mihintale Sanctuary, Sri Lanka

This study was focused on explaining food selectivity in endemic Sri Lanka Grey Hornbill Ocyceros gingalensis to fill the gaps in the behavioral ecology of this endemic species. The study was conducted within Mihintale Sanctuary for five months from December 2015 to April 2016. Ringed hornbills were used to monitor the number of food items that were consumed from within the Food Abundance Index (FAI) and quantify the distribution and availability of resources to examine the potential of fruit selectivity. Thirteen fruiting plant families were recorded as preferred food. Food consumption and FAI values are not significantly correlated (r = 0.60, p = 0.285). The dietary composition increased in the breeding season due to a higher requirement for nutrients by the nestlings. Nutrient analysis results revealed that moisture (H = 7.50, p = 0.006), fiber (H = 6.53, p = 0.011), and ash (H = 6.07, p = 0.013) components were significant between eaten and non-eaten fruits. The amount of all the nutrients available in the fruits as well as FAI does not directly affect the fruit selectivity of the Sri Lanka Grey Hornbill in the Mihintale Sanctuary. This fruit selection and the seed dispersal ability of the Sri Lanka Grey Hornbill contributes to maintaining the ecosystem diversity and forest regeneration, especially in the Dry Zone in Sri Lanka. 

Rainfall Variability and Tidal Inundation Influences on Mangrove Greenness in Karimunjawa National Park, Indonesia

Mangroves, which are vulnerable to natural threats and human activities on small islands in the tropics, play an essential role as carbon sinks, helping to mitigate climate change. In this study, we discussed the effect of natural factors on mangrove sustainability by analyzing the impact of rainfall, land surface temperature (LST), and tidal inundation on the greenness of mangroves in Karimunjawa National Park (KNP), Indonesia. We used Sentinel-2 image data to obtain the normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) during the dry season to determine the effect of inundation on mangrove greenness and soil moisture. The tidal inundation area was calculated using topographic data from the KNP and tidal observations from the area adjacent to it. Unmanned autonomous vehicles and topographic data were used to estimate mangrove canopy height. We also calculated mangrove greenness phenology and compared it to rainfall from satellite data from 2019–2021. Results show that the intertidal area is dominated by taller mangroves and has higher NDVI and NDMI values than non-intertidal areas. We also observed that mangroves in intertidal areas are mostly evergreen, and optimum greenness in KNP occurs from February to October, with maximum greenness in July. Cross-correlation analysis suggests that high rainfall affects NDVI, with peak greenness occurring three months after high rainfall. The LST and NDVI cross-correlation showed no time lag. This suggests that LST was not the main factor controlling mangrove greenness, suggesting tides and rainfall influence mangrove greenness. The mangroves are also vulnerable to climate variability and change, which limits rainfall. However, sea-level rise due to climate change might positively impact mangrove greenness.

Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand

The Lam Phaniang River Basin is one of the areas in Northeast Thailand that experiences persistent drought almost every year. Therefore, this study was focused on the assessment of drought severity and vulnerability in the Lam Phaniang River Basin. The evaluation of drought severity was based on the Drought Hazard Index (DHI), which was derived from the Standardized Precipitation-Evapotranspiration Index (SPEI) calculated for 3-month (short-term), 12-month (intermediate-term), and 24-month (long-term) periods. Drought vulnerability was assessed by the Drought Vulnerability Index (DVI), which relied on water shortage, water demand, and runoff calculated from the WEAP model, and the Gross Provincial Product (GPP) data. A drought risk map was generated by multiplying the DHI and DVI indices, and the drought risk level was then defined afterwards. The CNRM-CM5, EC-EARTH, and NorESM1-M global climate simulations, and the TerrSet software were used to evaluate the potential impacts of future climate under RCPs 4.5 and 8.5, and land use during 2021–2100, respectively. The main findings compared to baseline (2000–2017) revealed that the average results of future rainfall, and maximum and minimum temperatures were expected to increase by 1.41 mm, and 0.015 °C/year and 0.019 °C/year, respectively, under RCP 4.5 and by 2.72 mm, and 0.034 °C/year and 0.044 °C/year, respectively, under RCP 8.5. During 2061–2080 under RCP 8.5, the future annual water demand and water shortage were projected to decrease by a maximum of 31.81% and 51.61%, respectively. Obviously, in the Lam Phaniang River Basin, the upper and lower parts were mainly dominated by low and moderate drought risk levels at all time scales under RCPs 4.5 and 8.5. Focusing on the central part, from 2021–2040, a very high risk of intermediate- and long-term droughts under RCPs 4.5 and 8.5 dominated, and occurred under RCP 8.5 from 2041–2060. From 2061 to 2080, at all time scales, the highest risk was identified under RCP 4.5, while low and moderate levels were found under RCP 8.5. From 2081–2100, the central region was found to be at low and moderate risk at all time scales under RCPs 4.5 and 8.5. Eventually, the obtained findings will enable stakeholders to formulate better proactive drought monitoring, so that preparedness, adaptation, and resilience to droughts can be strengthened.

A Novel Taxonomy for Risks in Agribusiness Supply Chains: A Systematic Literature Review

Agribusiness supply chain (ASC) risk is currently a major business problem throughout the world. The current trend of globalisation has affected every business, and supply chain risks have become a concern in logistics and other business processes. Current risk management strategies must address a variety of global and local challenges. To tackle this issue, existing research has analysed risks in agrifood supply chains, ASC risk management, disruption in ASCs, risk assessments of agriculture supply chains and sources of risk facing an agricultural supply chain. However, the existing research has not defined and categorised risks as a basis for managing risks in ASCs. Therefore, the definition and categorisation of risks in the ASC has been overlooked. To address this gap, this paper undertakes a systematic literature review, offering constructs to define and categorise risks in ASCs, and develops a novel taxonomy in ASC risks to enrich future research on ASC risk management. Sixty-one articles from six databases published between 2000 and 2020 underwent descriptive and thematic analysis.