Digital artifacts reveal development and diffusion of climate research

Bia Carneiro Tek Sapkota (2022, [Artículo])

Accessible Knowledge Impact of Outputs Traditional Bibliometric Analyses Hyperlink Analysis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE DIFFUSION MAIZE MINING ORGANIZATION SOCIAL MEDIA SOCIAL NETWORK ANALYSIS WHEAT TEXT MINING

Towards gender-inclusive innovation: Assessing local conditions for agricultural targeting

Diana E. Lopez Romain Frelat Lone Badstue (2022, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURE CLIMATE FEMALES GENDER HUMANS GENDER EQUALITY

Emmer wheat eco-geographic and genomic congruence shapes phenotypic performance under mediterranean climate

Dana Fuerst SHAILESH YADAV Rajib Roychowdhury Carolina Sansaloni Sariel Hübner (2022, [Artículo])

Emmer Wheat CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT GENETIC VARIATION CLIMATE PHENOLOGY YIELDS MEDITERRANEAN CLIMATE

GWAS identifies genetic loci underlying nitrogen responsiveness in the climate resilient C4 model Setaria italica (L.)

Tirthankar Bandyopadhyay Stéphanie M. Swarbreck Vandana Jaiswal Rajeev Gupta Alison Bentley Manoj Prasad (2022, [Artículo])

C4 Model Crop Climate Resilience CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE RESILIENCE FOOD SECURITY GENE EXPRESSION NITROGEN

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

Production vulnerability to wheat blast disease under climate change

Diego Pequeno Jose Mauricio Fernandes Pawan Singh Willingthon Pavan Kai Sonder Richard Robertson Timothy Joseph Krupnik Olaf Erenstein Senthold Asseng (2024, [Artículo])

Wheat Blast Tropical Regions CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PLANT DISEASES CLIMATE CHANGE PRODUCTION

Hub meeting Honduras: Gestión de la red de innovación en el occidente y oriente de Honduras. ¿Porque genero e inclusion social en Agrilac?

Angela Meentzen (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENDER EQUALITY FOOD SYSTEMS CLIMATE CHANGE WOMEN'S PARTICIPATION

Impacts of climate change on agriculture and household welfare in Zambia: an economy-wide analysis

Hambulo Ngoma (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE SMALLHOLDERS ECONOMIC ANALYSIS

Mining alleles for tar spot complex resistance from CIMMYT's maize Germplasm Bank

Martha Willcox Juan Burgueño Daniel Jeffers Zakaria Kehel Rosemary Shrestha Kelly Swarts Edward Buckler Sarah Hearne Charles Chen (2022, [Artículo])

Maize Landraces Maize Genetic Resources Allelic Diversity Rare Alleles Phenotypic Characterization Tropical Maize Phyllachora maydis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE LANDRACES GENETIC RESOURCES ALLELES FOLIAR DISEASES CLIMATE CHANGE

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

Mustafa Kamal Timothy Joseph Krupnik (2024, [Artículo])

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.

Synthetic Aperture Radar Random Forest Boro Rice In-Season Maps CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SAR (RADAR) RICE FLOODING CLIMATE CHANGE