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User manual: How to use Agvisely to generate climate service advisories for livestock in Bangladesh

T.S Amjath-Babu Timothy Joseph Krupnik (2023, [Libro])

The Agvisely digital service for livestock integrates location-specific meteorological forecasts generated by the Bangladesh Meteorological Department (BMD) with species specific biological thresholds for weather variables (Temperature, rainfall, and temperature-humidity index (THI). When a biological threshold is to be breached in next five days' forecast, the system automatically generates location-specific management advice for livestock farmers. Advisories are based on a decision tree developed by the Bangladesh Livestock Research Institute (BLRI) and CIMMYT. Agvisely is a smart phone app and web-based service developed by the International Maize and Wheat Improvement Center (CIMMYT) CIMMYT with the support of USAID, securing the Food Systems of Asian Mega- Deltas (AMD) for Climate and Livelihood Resilience and the Transforming Agrifood Systems in South Asia (TAFSSA) initiatives in collaboration with Bangladesh Dept. of Agricultural Extension (DAE) and Bangladesh Meteorological Department (BMD).

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE SERVICES LIVESTOCK DIGITAL TECHNOLOGY

Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

Nachiketa Acharya Carlo Montes Timothy Joseph Krupnik (2023, [Artículo])

Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system.

Multi-Model Ensemble Seasonal Forecasting CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE SERVICES FORECASTING MONSOONS

Economic valuation of climate induced losses to aquaculture for evaluating climate information services in Bangladesh

Peerzadi Rumana Hossain T.S Amjath-Babu Timothy Joseph Krupnik (2023, [Artículo])

Very little research has focused on climate impacts on aquaculture and the potential of climate information services (CIS) for aquaculture to support sustainable development goals 2030 (SDGs)1. This study represents an effort to bridge this gap by conducting a first ex-ante economic evaluation of CIS for aquaculture in Bangladesh by semi-automating the extraction of data on climate-induced fish losses during 2011 to 2021 from popular online newspaper articles and corroborating them with available government and satellite datasets. During this period, Bangladesh faced an estimated loss of around 140 million USD for hatcheries, open water fish and shrimp. When validated with a year of country-wide official data on climate-induced economic losses to aquaculture, the damage reported from these media sources is approximately 10 percent of actual losses. Given this rule of thumb, the potential economic value of aquacultural CIS could be up to USD14 million a year, if 10 percent of the damage can be offset by appropriate services through a range of multi-sector efforts to establish and extend these services to farmers at scale.

Climate Information Services Newspaper Scraping CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LOSSES AQUACULTURE CLIMATE SERVICES SUSTAINABLE DEVELOPMENT GOALS

Do provisioning ecosystem services change along gradients of increasing agricultural production?

Ronju Ahammad Stephanie Tomscha Sarah Gergel Frédéric Baudron Jean-Yves Duriaux Chavarría Samson Foli Dominic Rowland Josh Van Vianen Terence Sunderland (2024, [Artículo])

Context: Increasing agricultural production shapes the flow of ecosystem services (ES), including provisioning services that support the livelihoods and nutrition of people in tropical developing countries. Although our broad understanding of the social-ecological consequences of agricultural intensification is growing, how it impacts provisioning ES is still unknown. Objectives: We examined the household use of provisioning ES across a gradient of increasing agricultural production in seven tropical countries (Bangladesh, Burkina Faso, Cameroon, Ethiopia, Indonesia, Nicaragua and Zambia). We answered two overarching questions: (1) does the use of provisioning ES differ along gradients of agriculture production ranging from zones of subsistence to moderate and to high agriculture production? and (2) are there synergies and/or trade-offs within and among groups of ES within these zones? Methods: Using structured surveys, we asked 1900 households about their assets, livestock, crops, and collection of forest products. These questions allowed us to assess the number of provisioning ES households used, and whether the ES used are functionally substitutable (i.e., used similarly for nutrition, material, and energy). Finally, we explored synergies and trade-offs among household use of provisioning ES. Results: As agricultural production increased, provisioning ES declined both in total number and in different functional groups used. We found more severe decreases in ES for relatively poorer households. Within the functional groups of ES, synergistic relationships were more often found than trade-offs in all zones, including significant synergies among livestock products (dairy, eggs, meat) and fruits. Conclusions: Considering landscape context provides opportunities to enhance synergies among provisioning services for households, supporting resilient food systems and human well-being.

Agricultural Production Zones Agricultural Intensifcation Synergies and Trade-Offs Landscape Multifunctionality Social-Ecological Systems CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE INTENSIFICATION ECOSYSTEM SERVICES LANDSCAPE SOCIAL-ECOLOGICAL RESILIENCE ECOSYSTEM SERVICES

Spatiotemporal analysis of rainfall and temperature variability and trends for climate resilient maize farming system in major agroecology zones of northwest Ethiopia

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

Spatiotemporal studies of the annual and seasonal climate variability and trend on an agroecological spatial scale for establishing a climate-resilient maize farming system have not yet been conducted in Ethiopia. The study was carried out in three major agroecological zones in northwest Ethiopia using climate data from 1987 to 2018. The coefficient of variation (CV), precipitation concertation index (PCI), and rainfall anomaly index (RAI) were used to analyze the variability of rainfall. The Mann-Kendall test and Sen’s slope estimator were also applied to estimate trends and slopes of changes in rainfall and temperature. High-significance warming trends in the maximum and minimum temperatures were shown in the highland and lowland agroecology zones, respectively. Rainfall has also demonstrated a maximum declining trend throughout the keremt season in the highland agroecology zone. However, rainfall distribution has become more unpredictable in the Bega and Belg seasons. Climate-resilient maize agronomic activities have been determined by analyzing the onset and cessation dates and the length of the growth period (LGP). The rainy season begins between May 8 and June 3 and finishes between October 26 and November 16. The length of the growth period (LGP) during the rainy season ranges from 94 to 229 days.

Climate Trends Spatiotemporal Analysis Agroecology Zone CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGROECOLOGY CLIMATE CLIMATE VARIABILITY MAIZE